Category: Machine-Learning

The effective use of information is one of the prime requirements for any kind of business operation. At some point, the amount of data produced goes beyond simple processing capacities. That’s where machine learning algorithms kick in.

However, before any of it could happen – the information needs to be explored and made sense of. That is what unsupervised machine learning is for in a nutshell.

We had talked about supervised ML algorithms in the previous article. In this one, we’ll focus on unsupervised ML and its real-life applications. 

What is unsupervised machine learning?

Unsupervised learning is a type of machine learning algorithm that brings order to the dataset and makes sense of data.

Unsupervised machine learning algorithms are used to group unstructured data according to its similarities and distinct patterns in the dataset.

The term “unsupervised” refers to the fact that the algorithm is not guided like a supervised learning algorithm.

How does an unsupervised ML algorithm work?

The unsupervised algorithm is handling data without prior training – it is a function that does its job with the data at its disposal. In a way, it is left at his own devices to sort things out as it sees fit.  

The unsupervised algorithm works with unlabeled data. Its purpose is exploration. If supervised machine learning works under clearly defines rules, unsupervised learning is working under the conditions of results being unknown and thus needed to be defined in the process.

The unsupervised machine learning algorithm is used to:

1. Explore the structure of the information and detect distinct patterns;
2. Extract valuable insights;
3. Implement this into its operation in order to increase the efficiency of the decision-making process

In other words, it describes information – go through the thick of it and identifies what it really is.

In order to make that happen, unsupervised learning applies two major techniques – clustering and dimensionality reduction.

Clustering – Exploration of Data

“Clustering” is the term used to describe the exploration of data, where similar pieces of information are grouped. There are several steps to this process:

• Defining the credentials that form the requirement for each cluster. The credentials are then matched with the processed data and thus the clusters are formed.  
• Breaking down the dataset into specific groups (known as clusters) based on their common features.

Clustering techniques are simple yet effective. They require some intense work yet can often give us some valuable insight into the data.

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Clustering has been widely used across industries for years:

• Biology – for genetic and species grouping;
• Medical imaging – for distinguishing between different kinds of tissues;
• Market research – for differentiating groups of customers based on some attributes
• Recommender systems – giving you better Amazon purchase suggestions or Netflix movie matches.

Dimensionality Reduction – Making Data Digestible

In a nutshell, dimensionality reduction is the process of distilling the relevant information from the chaos or getting rid of the unnecessary information.

Raw data is usually laced with a thick layer of data noise, which can be anything – missing values, erroneous data, muddled bits, or something irrelevant to the cause. Because of that, before you start digging for insights, you need to clean the data up first. Dimensionality reduction helps to do just that. 

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From the technical standpoint – dimensionality reduction is the process of decreasing the complexity of data while retaining the relevant parts of its structure to a certain degree.

7 Unsupervised Machine Learning Real Life Examples

k-means Clustering – Data Mining

k-means clustering is the central algorithm in unsupervised machine learning operations. It is the algorithm that defines the features present in the dataset and groups certain bits with common elements into clusters.

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As such, k-means clustering is an indispensable tool in the data-mining operation. It is also used for:

• Audience segmentation
• Customer persona investigation
• Anomaly detection (for example, to detect bot activity)
• Pattern recognition (grouping images, transcribing audio)
• Inventory management (by conversion activity or by availability)

Hidden Markov Model – Pattern Recognition, Natural Language Processing, Data Analytics

Another example of unsupervised machine learning is the Hidden Markov Model. It is one of the more elaborate ML algorithms – a statical model that analyzes the features of data and groups it accordingly.

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Hidden Markov Model is a variation of the simple Markov chain that includes observations over the state of data, which adds another perspective on the data gives the algorithm more points of reference.

Hidden Markov Model real-life applications also include:

• Optical Character recognition (including handwriting recognition)
• Speech recognition and synthesis (for conversational user interfaces)
• Text Classification (with parts-of-speech tagging)
• Text Translation

Hidden Markov Models are also used in data analytics operations. In that field, HMM is used for clustering purposes. It finds the associations between the objects in the dataset and explores its structure. Usually, HMM are used for sound or video sources of information.

DBSCAN Clustering – Customer Service Personalization, Recommender engines

DBSCAN Clustering AKA Density-based Spatial Clustering of Applications with Noise is another approach to clustering. It is commonly used in data wrangling and data mining for the following activities:

• Explore the structure of the information
• Find common elements in the data
• Predict trends coming out of data

Overall, DBSCAN operation looks like this:

• The algorithm groups data points that are close to each other.
• Then it sorts the data according to the exposed commonalities

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DBSCAN algorithms are used in the following fields:

• Targeted Ad Content Inventory Management
• Customer service personalization
• Recommender Engines

Principal component analysis (PCA) – Data Analytics Visualization / Fraud Detection

PCA is the dimensionality reduction algorithm for data visualization. It is a sweet and simple algorithm that does its job and doesn’t mess around. In the majority of cases is the best option.

In its core, PCA is a linear feature extraction tool. It linearly maps the data about the low-dimensional space.

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PCA combines input features in a way that gathers the most important parts of data while leaving out the irrelevant bits.

As a visualization tool – PCA is useful for showing a bird’s eye view on the operation. It can be an example of an excellent tool to:

• Show the dynamics of the website traffic ebbs and flows.
• Break down the segments of the target audience on specific criteria

t-SNE – Data Analytics Visualization

t-SNE AKA T-distributed Stochastic Neighbor Embedding is another go-to algorithm for data visualization.

t-SNE uses dimensionality reduction to translate high-dimensional data into low-dimensional space. In other words, show the cream of the crop of the dataset.  

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The whole process looks like this:

• The algorithm counts the probability of similarity of the points in a high-dimensional space.
• Then it does the same thing in the corresponding low-dimensional space.
• After that, the algorithm minimizes the difference between conditional probabilities in high-dimensional and low-dimensional spaces for the optimal representation of data points in a low-dimensional space.

As such, t-SNE is good for visualizing more complex types of data with many moving parts and everchanging characteristics. For example, t-SNE is good for:

• Genome visualization in genomics application
• Medical test breakdown (for example, blood test or operation stats digest)
• Complex audience segmentation (with highly detailed segments and overlapping elements)

Singular value decomposition (SVD) – Recommender Systems

Singular value decomposition is a dimensionality reduction algorithm used for exploratory and interpreting purposes.

It is an algorithm that highlights the significant features of the information in the dataset and puts them front and center for further operation. Case in point – making consumer suggestions, such as which kind of shirt and shoes fit best with those ragged Levi’s jeans.

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In a nutshell, it sharpens the edges and turns the rounds into tightly fitting squares. In a way, SVD is reappropriating relevant elements of information to fit a specific cause.

SVD can be used:

• To extract certain types of information from the dataset (for example, take out info on every user located in Tampa, Florida).
• To make suggestions for a particular user in the recommender engine system.
• To curate ad inventory for a specific audience segment during real-time bidding operation.

Association rule – Predictive Analytics

Association rule is one of the cornerstone algorithms of unsupervised machine learning.

It is a series of techniques aimed at uncovering the relationships between objects. This provides a solid ground for making all sorts of predictions and calculating the probabilities of certain turns of events over the other.

While association rules can be applied almost everywhere, the best way to describe what exactly they are doing are via eCommerce-related example.

There are three major measure applied in association rule algorithms

• Support measure shows how popular the item is by the proportion of transaction in which it appears.
• Confidence measure shows the likeness of Item B being purchased after item A is acquired.
• Lift measure also shows the likeness of Item B being purchased after item A is bought. However, it adds to the equation the demand rate of Item B.

Conclusion

The secret of gaining a competitive advantage in the specific market is in the effective use of data. Unsupervised machine learning algorithms help you segment the data to study your target audience’s preferences or see how a specific virus reacts to a specific antibiotic. 

Real-life applications abound and our data scientists, engineers, and architects can help you define your expectations and create custom ML solutions for your business. 

Any technology, taking progress several steps forward, always raises concerns that border on excitement and disappointment at the same time. This trend has not spared artificial intelligence. Even though the technology is not new (the first solutions appeared in the 1960s), the real breakthrough and active use of AI in business appeared only in the 21st century. Computing power, larger datasets, and the rise of open-source software allowed developers to create advanced algorithms.

Nowadays, almost all businesses want AI, regardless of size and tasks. So let’s see if artificial intelligence is really so beneficial. For whom it’s too early to implement it, and who needs it as of yesterday.

AI Application for Business

Artificial intelligence is an imitation of the mental properties of the human brain by computer systems. The algorithm learns itself, becoming more and more perfect. To reach the level of a full-fledged thought process, still enough time must pass (although some experts argue that a machine and a person will equal in intellectual abilities in the next decade).

Project Management For Software Development: The App Solutions Tips

Nevertheless, AI is designed to solve relatively voluminous and straightforward tasks from document flow to primitive communication as support. These AI capabilities alone save businesses around the world thousands and thousands of labor hours. Already, 72% of companies using AI in their work say that it makes doing business easier.

In this regard, there are fears that many people will be left without work. Indeed, according to forecasts, secretaries, accountants, administrators, auditors, repairers in factories, and even general and operational managers can lose their jobs. In contrast, new jobs will receive big data and data analysts specialists, AI and machine learning engineers, software and app developers… 

The World Economic Forum says 85 million jobs will be eliminated by 2025, while 97 million new jobs will appear. So the reformatting of the labor market towards technical specialties is inevitable one way or another.

According to Fortune Business Insights, the global AI market was estimated at $27.23 billion in 2019 and is projected to reach $266.92 billion by 2027, with a 33.2% CAGR over the forecast period.

At the same time, IDG claims that in 2021 the cost of AI and similar systems reaches $57.6 billion. For instance, Netflix spends $1 million annually to develop its recommendation engine. According to company representatives: “The typical Netflix contributor loses interest after about 60-90 seconds of selection, having watched 10 to 20 titles (possibly 3 in detail) on one or two screens.” It’s cheaper to spend money on a good advisor than to lose views.

The PwC’s forecast claims that in 2030 AI can contribute up to 15.7 trillion dollars to the global economy. For comparison, the combined output of China and India in the world economy is currently less. However, the PwC predicts that an attractive, innovative business that has yet to emerge could become a market leader based on AI technology in ten years.

AI is already used by 38% of healthcare providers as computer diagnostics and 52% of telecommunications companies as chatbots. It is not surprising. Consumers are increasingly demanding round-the-clock support and are ready to receive more primitive but instant answers to their questions; that is, they are ready to sacrifice quality to save their time.

Benefits of AI for Business 

Regardless of what field you work in – from law to marketing, from medicine to restaurant business – AI will find an appliance everywhere. Several undeniable benefits of AI will be effective for any business.

1. Improving customer engagement. Chatbot has already become the most popular way to communicate with consumers. Enterprise artificial intelligence contributes to increased customer satisfaction, leading to lower costs, in particular, on the payroll. Moreover, chatbots have become a real salvation for small businesses, which do not have the opportunity to hire a large staff for support.
2. Increased brand loyalty. Personalization is the key to the consumer’s heart, as evidenced by the investment of Netflix mentioned above in personalized search. With an individual approach, you will inevitably win the preferences of your customers, making them permanent. But to solve this problem, you need to collect a considerable array of analytics of behavioral factors. AI can solve it. Various studies say that this approach increases conversions from 14 to 30%.
3. Data security and fraud prevention. Primarily relevant for financial enterprises. AI not only finds weaknesses in security systems but can also determine the characteristic behavior during transactions.
4. Improving the accuracy of forecasts. Artificial intelligence allows you to avoid the human factor when making decisions, reducing the risk of mistakes. For example, lead scoring analyzes and predicts which leads will be the most promising. Other algorithms help control financial flows and trade. Also, you can be sure of compliance with all requirements, standards, and regulations that your company sets.
5. Recruiting optimization. By automating the analysis of candidates’ CVs, human bias in preliminary checks is eliminated. In due course, PepsiCo needed to hire 250 people out of 1,500 applicants in two months. AI was drawn into the first round of interviews. Thus, all candidates were interviewed in nine hours. It would take human personnel nine weeks, by contrast. During this time, “live” recruiters could deal with more complex creative tasks. The latter concerns other employees of your company – let them develop while AI does the whole routine for them.

How to Get the most out of AI Benefits 

There will be no benefits at all if enterprise AI software is not implemented efficiently. To prevent this in business processes, it is better to follow a few tips that will allow you to comprehensively approach the implementation of artificial intelligence.

1. New technologies need new people. Without hiring the appropriate specialists, only with the forces of the old state, you most likely will not succeed. Probably, you will need a whole department, but do not be afraid of such expenses – they pay off significantly. Of course, you can use the already developed AI technologies that other companies offer. Still, sooner or later, almost any business comes to the point when it becomes unprofitable and even unsafe to use third-party services.
2. Don’t be afraid to expand. The introduction of new technologies should bring benefits and profits to the business. However, to reduce costs, in the end, they will need to be increased first. And it concerns not only the increase in staff but also the expansion of new markets because with AI, it is possible to work with large amounts of information. Accordingly, new expenses cannot be avoided; however, the competent use of AI will very soon turn your expenses into income.
3. Don’t be afraid to change your motion vectors. Artificial intelligence often helps business owners understand that changing the business model will help them move on with greater efficiency. There is no need to be afraid to change anything because it is to change for the better that you started working with AI, right?

Signs Your Enterprise Needs AI Solutions 

Artificial intelligence is complex, and many businesses still don’t know how to implement and benefit from this technology. Companies around the world are at different stages of AI adoption:

• Awareness (there is the only talk of introducing AI when planning business processes and strategies)
• Activation (technology is not yet widely used, only as a test for some pilot projects)
• Operation (at least one project from start to finish uses AI in its work, a separate budget and team is allocated for this)
• Consistency (all new projects, products, and services are created using AI, all technical employees of the company are aware of the nuances of work, actively apply technology in their daily routine)

However, not all companies decide to implement AI, even if they see an obvious benefit. To understand if you really need AI, think about the following things.

Data Mining Vs. Predictive Analytics: Know The Difference

Well-established Data Collection 

Determine how much information your employees will have to work with. You won’t be able to endlessly hire new specialists to cover all your database needs. If the costs of implementing AI outweigh the other concerns, then prepare your data to ensure that AI adoption runs as smoothly as possible. This requires:

1. Keep data up to date. The algorithm will not be able to make accurate predictions and provide relevant analytics if your data, for example, on customer behavioral factors, is not updated. To put it bluntly, you don’t need a smartphone if you still use pigeon posts. Spending on AI should pay off. It can process huge amounts of information and produce specific results, but who will need them if the initial data is outdated.
2. Check your details for errors. A machine can process a large amount of data faster than you, but at the same time, it can get confused about some elementary thing that a first-grader would easily understand. Where the human brain sees a typo in the same word, the machine sees two different words. Of course, the AI ​​has reached the level where it realizes that you made a mistake (for example, when the search engine suggests that “you must have meant” something completely different). However, the search engine has enough experience to conclude the error, but will your algorithm have enough experience from scratch?
3. Use a consistent format for storing data. For AI to collect all the information stored in your company and process it correctly, you should contain it in one setup.

Particular Business Problem to Solve 

So, you have prepared the technical basis for the AI implementation, and now you need to decide what algorithm can help you in the first place? Perhaps, to solve critical problems, or are you already doing well, but you want it to be even better?

1. Increase the price of the existing product. As we mentioned earlier, the attractiveness of a product or service increases not only due to the quality of the product in front of competitors but also due to a personalized approach to the client. Are you selling cosmetics? Let your AI match the eyeshadow palette, mascara, or 50 shades of lipstick from the same producer to the one chosen by the client.
2. Analysis of the current status of the business. The algorithm can help you find weaknesses that you didn’t even know about: logistics, marketing, sales, manufacturing – all these can be bottlenecks. Plan resources and forecast demand correctly with AI technology.
3. Business process automation. When you have identified and eliminated the problems and perhaps even radically changed the business model, it’s time to think about automating processes and, accordingly, optimizing the staff and retraining for more intelligent work.

Culture of Innovations 

Before implementing AI, make sure that your employees share a philosophy of innovation and progress with you, that they have no fear of not coping and fear for their workplace. New technologies can be quickly, organically, and painlessly introduced only if your company is constantly engaged in them.

1. Corporate strategy. Don’t innovate for the sake of innovation. You will never make a profit this way. You should not put all products under the auspices of AI at once. Try with small, not very resourceful projects. Then there is no risk that your company will collapse like a house of cards in case of failure.
2. Metrics. Be sure to define the criteria by which you will measure the success of the implementation of AI to understand when the payback comes.
3. The right to make mistakes. Yes, this also needs to be incorporated into the business strategy. One of the indisputable advantages of AI is considered to be that it excludes the human factor. However, the machine can malfunction; this is a well-known fact. Do not assume that this risk negates all the other advantages of a smart algorithm. Just take into account that you need to spend money not only on the development but also on the support of the algorithm at first.

Outcomes

For all the attractiveness of AI technology, consider whether you really need it. Do your capacities give reason to implement it? If the amount of information is large and the corporate business strategy and tasks are flexible enough, there is no point in delaying.

The APP Solutions is a web and mobile app development team aware of AI algorithms development and implementation for Enterprises. Suppose you are already ready to introduce AI technologies into your business but cannot decide on a development team. In that case, we are ready for fruitful cooperation and are waiting for you!

Artificial Intelligence is a compound, highly complex technology with almost unlimited possibilities, including many structural elements and subsets. Each of them is necessary to perform specific tasks, independently or in combination with others. In this article, we will talk about such subsets as predictive analytics and machine learning. We will analyze what they have in common, or different, where they are used and why one does not replace but complements the other.

 

Predictive Analytics Definition

Predictive analytics forecast the future based on data gathered in the past to find likely patterns and behaviors. It reduces errors by removing the notorious human factor and bringing out important ideas and trends. The term “predictive analytics” refers to an approach, not a specific technology.

How Does it Work?

Techniques used in predictive analytics include descriptive analytics, advanced statistical modeling and mathematics, high-volume data mining, and AI algorithms. For this large volume to be quickly and efficiently analyzed, machine learning is needed.

Predictive analytics is based on prognostication modeling. It is more a scientific niche than a process. Predictive analytics and machine learning go hand in hand since predictive models usually include a machine learning algorithm. These models can be trained over time to respond to new data or values ​​to provide the results your business needs. Predictive modeling has a lot in common with machine learning but is not an identical phenomenon.

Machine Learning Definition

Machine learning is an AI tool that makes it possible to improve forecasting accuracy without additional coding. The machine exercises this by detecting specific patterns in the data clusters. The tool automates predictive modeling by creating training algorithms to find consistency and behavior in data without clearly specifying the search meaning.

How Does it Work?

Machine learning includes drilling algorithms, neural networks, or processing computers to analyze data and automatically output results at the desired scale. Machine learning usually works by combining large amounts of data through iteration and intelligent algorithms, allowing software to automatically learn from patterns or data functions.

Machine learning’s ability to learn from previous datasets and remain flexible allows for various applications, not just predictive modeling.

Predictive Analytics vs. Machine Learning: Similarities

The main similarity between predictive analytics and machine learning can be called a reference to the past to unravel the future. But the significance, approaches, and functions in this process are somewhat different.

Other common criteria include: 

• the use of an extensive array of data that a person cannot cope with
• analysis of patterns (albeit in a different way) to determine future results
• application in the same business sectors: security, finance, retail, medicine, etc.

Even though we present machine learning and predictive analytics as related areas of AI, there are still significantly more differences.

Predictive Analytics vs. Machine Learning: Difference

Let’s immediately define that predictive analytics and machine learning are different categories of a very generalized concept of AI. Machine learning is a technology that works with complex algorithms and vast amounts of data. At the same time, predictive analysis is research, not a specific technology that existed long before the advent of machine learning; it just made it much more efficient and accurate. 

Simply put, machine learning is a method that has catalyzed progress in the predictive analytics field, while predictive analytics is one of the machine learning applications. There is no problem that predictive analytics can solve, but machine learning cannot.

Benefits and challenges of predictive analytics and machine learning in business

Any AI methods used in business, sooner or later, give tangible results. Therefore, it is only important to understand to what extent these methods and technologies “come to the court” in your case. In some cases, the use of AI pays off relatively quickly; in others, its use is redundant, and the company is neither technically nor “humanly” ready for such a transition to a new level. 

Let’s talk about the pros and cons of machine learning and predictive analytics and some use cases to understand how valuable this tool will be for you and what it has to offer.

Does Your Business Really Need An Enterprise Artificial Intelligence

Predictive Analytics and Machine Learning Advantages 

 

• Automation of processes and, as a result, saving time and money
• Improving economic performance through a well-thought-out financial strategy and logistics
• Getting into the vanguard of a niche due to the ability to foresee the global business trend and understand behavioral factors
• Technology consolidation, simplifying processes for end-users

 

Predictive Analytics Disadvantages

 

• The need to collect an impressive amount of data to get a relevant forecast
• You need to keep all trends and patterns that were derived earlier
• Is guided only by the historical data set, not taking into account current information
• The unpredictability of human behavior in some aspects can give an inaccurate forecast (for example, if, as a result of an image scandal, the company’s indicators sagged at the moment)

 

Machine Learning Disadvantages

 

• The problem must be very descriptive to find the correct algorithm to apply the solution
• Big data requirements and training data, such as deep learning data, must be created before this algorithm is actually used
• resource costs for technology are not always economically feasible

 

Although there are more disadvantages, the weight of the seemingly small advantages is much higher. We will prove this by briefly describing how and in which business areas both phenomena are used.

Predictive Analytics: when used

Predictive analytics is used to detect trends in behavioral factors across various sites to personalize email advertising messages. Impressive sets of information are collected in a variety of ways, not just online. These can be sensors in retail outlets or store applications, completed questionnaires indicating email, and social networks. All this adds up to the idea of sales forecasting, logistics, and customer experience management.

Predictive analytics works with both people and mechanisms. For example, with its help, you can predict buyer behavior or the growth of a specific disease among certain groups of the population, identify the employees of your company who are thinking about dismissal, or calculate the bank’s clients who are facing bankruptcy soon.

You can predict the wear and tear of equipment or the percentage increase in fraudulent transactions among a series of such bank operations.  

Using machine learning, predictive analysts can work with not only historical data, but also current data.

Data Mining Vs. Predictive Analytics: Know The Difference

 

Machine Learning: when used

Machine learning is less about reporting than about modeling itself. It is not required to answer human questions.

Examples of using machine learning: 

• Identifying patterns in marketing research
• Flagging errors in transactions or data entry
• Automatic subtitles in videos
• Personalized shopping experience based on browsing history
• Signaling anomalies in medical research

Conclusions

Machine learning is a tool, and predictive analytics is a role equipped with tools, one of which is machine learning. These are interacting concepts.

Machine learning algorithms can produce more accurate predictions, generate cleaner data, and enable predictive analytics to run faster and provide deeper insights with less control. Having a solid predictive analysis model and clean data fosters the development of machine learning applications.

To get the most out of predictive analytics and machine learning, organizations need to make sure they have an architecture that supports these solutions and high-quality data to help them learn. They should be centralized, unified, and in a consistent format. In addition, organizations need to know what problems they want to solve as this will help them determine the best and most applicable model to use. This will increase efficiency at all stages of the business. Providing the best practice, The APP Solutions can help with this!

Credits to Depositphotos

As life expectancy increases, healthcare organizations face an increasing demand for their services, rising costs, and a labor force struggling to meet the needs of their patients. By 2050, one in four people in Europe and North America will be over 65, which means the healthcare system will have to deal with many patients with complex needs. Managing these patients is costly and requires systems to move from an episodic-based service to a more management-oriented long-term care.

 

AI Technology of Healthcare Providers

Artificial intelligence based on automation can revolutionize healthcare and help to solve vital problems. Few technologies are advancing as rapidly as AI in the healthcare industry. AI is now used in many life spheres, but the health-care industry was one of the first to use it widely. According to Statista, from 2016 to 2017, the AI ​​market in healthcare grew by $ 500 million (from 1 to 1.5 billion), and by 2025 is predicted to grow to 28 billion.

An even more optimistic forecast is given by Tractica – by 2025, growth is projected to be 34 billion, and by 2030 to 194.4 billion.

All of these investments include case studies on patient data processing and management, and transformation from paper to digital format, digital image interpretation (for example, in radiology, ophthalmology, or pathology), diagnosis and treatment, biomarker discovery, and drug efficacy calculations.

Forbes says AI tools are already being implemented in 46% of service operations, 28% in product and service development, 19% in risk management, 21% in supply chain management, and 17% in marketing and sales in the healthcare industry.

North America dominated the healthcare AI market with the largest share of revenues at 58.9% in 2020. Factors which determine the market in the region are a broader adoption of AI technologies, growing licensing and partnerships, and favorable government initiatives.

AI has proven to be an important resource for evaluating patient scan data and identifying treatment options throughout the pandemic. It has also been also used to improve the administrative operations of hospitals and health centers. As a result, we may see more business applications from healthcare providers for more widespread use in medical procedures. 

How To Make A Medical App In 2021: The Ultimate Guide

EIT Health and McKinsey, in their report 2020, drew attention to which areas of medicine artificial intelligence is most often used.

As you can see, first of all, these are diagnostic tests and clinical research. However, a large amount of investment is also spent on technologies related to managing the way hospital’s function. Education and prescription automation are also included.

For example, AI is already being used to more accurately detect diseases such as cancer in their early stages. According to the American Cancer Society, most mammograms give false results. One in two healthy women are being told they have cancer. Using AI, mammograms can be viewed and translated 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies.

Three Phases of Scaling

AI in healthcare is a pervasive technology that can be successfully applied at different levels, depending on the complexity of the development.

 

First Phase

AI is solving routine paper, managerial, administrative processes that take time for doctors and nurses.

Second Phase

Remote monitoring. According to Accenture, artificial intelligence and machine learning can help meet 20% of all clinical requirements by reducing unnecessary clinic visits. At the same time, it is possible to reduce the number of readmissions to hospitals by 38%. 

As AI in healthcare improves, patients will take more and more responsibility for their treatment. Already, successful developments are being applied in such complex fields of precision medicine as oncology, cardiology, or neurology. For example, clinicians can be virtually close to their patients and observe certain conditions without personal visits.

This technology has proven to be especially useful during the pandemic when personal care was limited, but patients still needed support from their medical providers. 

Third Phase

AI in healthcare will become an integral part of the healthcare value chain, from learning, researching, and providing care, to improving public health. The integration of broader datasets across organizations, and robust governance for continuous quality improvement, are essential prerequisites for greater confidence among organizations, clinicians, and patients for managing risk when using artificial intelligence solutions.

 

AI Tools

Artificial intelligence is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP), deep learning (DL), and others enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster and with more accuracy.

Does Your Business Really Need An Enterprise Artificial Intelligence

AI vs. COVID-19: Patient Outcomes

Artificial intelligence technologies have played a critical role in the ongoing pandemic and positively impacted connected markets. It is used to quickly detect and diagnose virus strains and combat outbreaks with personalized information. For example, AI algorithms can be trained using chest CT images, infection history, symptoms, clinical documentation, and laboratory data to quickly diagnose COVID-19 positive patients.

In 2020, an NCBI study found that an artificial intelligence system identified 17 out of 25 COVID-positive patients based on typical computed tomography images, while experts diagnosed all patients as COVID-negative.

Thus, AI-based diagnostics can be used to accurately detect the disease even before the onset of apparent symptoms. In addition, these systems can be trained to analyze images and patterns to create algorithms to help healthcare professionals diagnose the disease accurately and quickly, thereby increasing the spread of AI technologies in healthcare. This will significantly reduce the load on the system and improve patient outcomes.

Related readings:

Calmerry Online Therapy Platform

Orb Health – Сare Management As A Virtual Service

BuenoPR – 360° Approach to Health

  

Benefits of AI/Machine Learning in Healthcare 

There are several areas in which AI has excelled, especially significantly helping doctors and medical institutions with the challenges that are becoming more and more in the modern world.

Predictive Analytics

With the rapid growth of medical knowledge, it is becoming increasingly difficult for doctors to keep up with the times. AI solutions that extract relevant medical expertise for each patient, and present it in a structured way can help clinicians choose the best treatment option, saving time and leading to more complex fact-based decision-making.

In a routine clinical setting, AI models can also detect patients at high risk of complications, or early deterioration, and provide recommendations to further support clinical decision-making with the prevention or early intervention. Reducing complications through early intervention can lead to improved health outcomes and reduced length of hospital stay and associated health care costs.

Predictive Analytics Vs. Machine Learning: What Is The Difference

AI can help identify a patient’s condition and recommend possible care and treatment options. This can save physicians doing research and, in turn, spending more time evaluating the possibilities presented by the AI ​​and discussing them with the patient.

One successful, and most importantly, relevant examples (in the midst of COVID) is a technology that predicts the oxygen levels of each patient. The engine indicates oxygen requirements within 24 hours of arrival in the emergency department with a sensitivity of 95% and specificity of over 88% based on previously examined X-rays. Software is being created that makes the work of radiologists unrealistically easier. 

Data Mining Vs. Predictive Analytics: Know The Difference

In the end, AI in healthcare could create a complete “home” version. For example, technologies already make it possible to produce “smart” toilets that could analyze urine and feces “on the spot.” Another question is that it is unlikely that the invention will have many fans at this stage of human development.

However, this extravagant decision could free up many laboratory specialists involved in this type of analysis for more complex work. And if you look more into the future, doctors will treat the consequences of patients who were too lazy to check urine tests on time (which they could have done without even leaving home).

Storing and Organizing Patient Data Bases

AI, in particular machine learning, can also be used with large datasets to predict health outcomes, helping healthcare systems focus more on prevention and early detection, improve health outcomes and, over time, make health care systems financially sustainable.

The big data automation capabilities, and real-time analytics built into syndromic surveillance, provide the information you need to understand disease progression and predict its risk to patients before it occurs. In addition, track disease symptoms, better manage public population health by predicting hospital utilization, geographic leaps, and associated material and resource requirements.

Using AI to analyze large datasets can be helpful in both healthcare settings and epidemiological research. AI models based on clinical data from a large population (e.g., patients in a healthcare region or an integrated healthcare provider system) can help identify early risk factors and initiate preventive action or early intervention at the system level. 

They can also help prioritize during times of staff shortage. Likewise, identifying an increased risk of unplanned hospitalization can help clinicians proactively intervene and avoid them.

Analysis of Digital Images

Radiologists and cardiologists make it much easier for themselves to work with images and scans, thanks to the capabilities of AI. Technological advances in this area allow you to prioritize critical cases, avoid potential errors in reading electronic health records (EHR data) and electronic medical records (EMR) and establish more accurate diagnoses.

AI algorithms can analyze big data sets quickly and compare them with other studies to uncover patterns and hidden relationships. This process allows medical imaging professionals to track critical information swiftly.

The Patient Brief examines past diagnoses and medical procedures, laboratory findings, medical history, and existing allergies and provides radiologists and cardiologists with a summary that focuses on the context of the images. The product can be integrated with any structure of the medical unit’s system, accessible from any communication workstation or some medical devices on the neural networks, and be updated without affecting the daily activities of the medical department.

AGILE HEALTHCARE: HOW TO IMPLEMENT THE APPROACH

AI and Pharmaceuticals

Another truly revolutionary example of the positive uses of AI in healthcare is drug research and discovery; one of the most recent AI applications in healthcare. By channeling the latest advances in AI to streamline drug discovery and repurposing processes, both the time to market for new drugs and their cost can be dramatically reduced.

Supercomputers have been used to predict, based on databases of molecular structures, which potential drugs will, or not, be effective for various diseases. AI and machine learning algorithms can identify new drugs, track their toxic potential and mechanisms of action. This healthcare technology has led to creating a drug discovery platform that allows the company to repurpose existing drugs.

Identifying new uses for known drugs is another attractive strategy for large pharmaceutical companies since it is cheaper to repurpose and relocate existing drugs than to create them from scratch.

KEEP A PULSE ON EPIC APP ORCHARD AND HOW IT BENEFITS THE HEALTH SYSTEMS

AI and Genetics

Altered molecular phenotypes, such as protein binding, contribute to genetic diseases. Therefore, predicting these changes means predicting the likelihood of a genetic disorder. This is possible due to data collection on all identified compounds and biomarkers relevant to specific clinical trials.

This allows us to recognize genetic abnormalities in the fetus and compose an individual treatment for a person with sporadic congenital disease.

AI in the Healthcare Apps

The growing popularity of smartphones and AI technologies among patients and professionals is driving the proliferation of virtual assistants. In addition, robotic surgery has been the most promising segment in the AI healthcare market as of 2020. This is mainly because surgical robot manufacturers are entering numerous strategic partnerships with data science and analytics companies and artificial intelligence technology providers.

The leading players in the AI ​​market:

• IBM Corporation
• NVIDIA Corporation
• Nuance Communications, Inc.
• Microsoft
• Intel Corporation
• DeepMind Technologies Limited

Healthcare Mobile Apps Development: Types, Examples, And Features

Future of AI/Deep Learning in Healthcare: Perspectives

According to The World Health Organization forecasts, the number of medical workers is steadily decreasing every year, and by 2030 there will be a shortage of almost 10 million professionals. AI, machine learning systems, and NLP can transform the way care is provided, meeting the need for better, more cost-effective care and helping to fill some of this gap in staffing. This is especially true as the population ages and health needs become more complex.

As the next step in telemedicine, Telesurgery aims to help reduce the damage caused by staff shortages. Telehealth, or virtual meeting, has become more widely used during the pandemic. This service has been used by those living in remote areas for several decades, but regularly by telephone rather than video conferencing. 

With the pandemic and the need for social distancing, telemedicine has become an integral part of healthcare services. Therefore, it has improved significantly as a result of the demand throughout the pandemic. Telesurgery is a field that is being researched and can be used in the provision of emergency care.

The current use of robotics in surgery allows physicians to perform minimally invasive surgeries and limits the impact of the procedure, improving outcomes. Expansion of surgery automation will continue to include AR and VR for increased productivity. 

Telesurgery is the next step being researched and provides access to a surgeon who does not specialize in the patient’s area of ​​residence. This saves the patient from traveling and can also be used when the patient requires immediate assistance. Problems may include delay and the need for a surgical team to support the procedure if a problem arises.

AI and automation are uniquely positioned to understand these needs and the complex interdependencies between various factors affecting public health. In addition, the extraordinary shift from symptom-based medicine, to molecular and cellular medicine, is generating ever-growing data amounts.

WHAT IS FEMTECH IN HEALTHCARE

The pace of change in AI within healthcare has accelerated significantly over the past few years thanks to advances in algorithms, processing power, and the increasing breadth and depth of data that can be used. In response, countries, health systems, investors and innovators are now focusing their attention on the topic.

Global venture capital funding for AI, ML, and deep learning in healthcare has reached $ 8.5 billion for 50 companies as clinical trials of AI healthcare applications increase.

And although AI will not be able to replace medical personnel (especially doctors) entirely, however, with the gradual introduction of technologies, the work of doctors will change only in a positive direction:

• More time for patients – less for paperwork (time optimization from 20 to 80%)
• Acceleration and improvement of diagnostics (especially in such fields as radiology, ophthalmology, pathology)
• Assistance in prioritizing the complexity of a patient’s condition (e.g., determining the likelihood of a heart attack, septic shock, respiratory failure)
• Improving the soft skills of clinicians by changing the format of communication with patients (people with chronic diseases can be served from home thanks to telemedicine)
• Increased educational level (while less severely ill patients can be treated remotely, the hospital will mainly admit patients with more complex cases, which requires more advanced skills from doctors)

 

AI bias in Healthcare: Disadvantages and Challenges

AI does not always become the optimal solution and salvation from all problems. This happens for several reasons:

• Insufficient development of technologies (moreover, several companies can solve the problem at once, but in the end, none of them will make a high-quality product that can be immediately thrown onto the market). The solution could be the unification of diverse teams that could consider all the necessary nuances.
• Changes in the medical education system around the world (the more technological solutions that can be offered to doctors, the more technically savvy they will have to be, and even top medical universities have not yet reorganized these new realities. Changes in patient behavior caused by AI also implies a change in the relationship between patients and practitioners, with the latter needing more attention to counseling and interpersonal skills).
• Databases (healthcare is one of the minor digitized sectors of the economy. Healthcare providers and AI companies need to implement robust data management, ensure interoperability and standards for data formats, improve security, and clarify consent to exchange health data).
• Regulation and risk management (defining the regulatory framework for AI in healthcare is significant for solving possible problem situations in which it is difficult to determine the degree of responsibility of all parties to the conflict).

 

Summary

AI in medicine still has many different stages to go through; the improvement process is only gaining momentum. But positive results are already visible. There are still fears that the excessive interference of technology will make the medical field less “human,” but only people who have not delved into the issue can speak this way. The more technologies are used in medical diagnosis, prevention, and treatment, the more time an actual doctor has directly for the patient.

Many medical and health apps help people self-diagnose their health, which ultimately allows doctors to focus on treatment. The development of such applications is carried out by companies with high expertise, including The APP Solutions. We are a highly skilled app development company who can bring your ideas to life, and we look forward to meeting you. If you have an interesting idea but are still contemplating how to implement it, contact us, we can help.

Credits to Depositphotos

Robotic process automation in healthcare is a rapidly growing AI technology with the potential to transform the healthcare industry. Many healthcare organizations are turning to RPA to streamline repetitive processes and improve efficiency. With the unique solution RPA offers, healthcare providers can focus more on improving the patient experience.

However, some healthcare practitioners still need to learn how powerful and beneficial robotic process automation can be. In this blog post, we will discuss the value and major use cases of RPA. We will also look at how healthcare automation can improve your organization.

Why is Robotic Process Automation Important to the Healthcare Industry?

Robotic process automation plays an integral role in boosting the overall performance of healthcare systems. It has the ability to complete tedious, time-consuming tasks accurately and provides numerous benefits to the healthcare industry. RPA increases administrative efficiency, boosts productivity, reduces costs, and eliminates mundane tasks without much human intervention.

It enables healthcare providers to focus on more essential patient-facing tasks while streamlining operational processes.

RPA use can provide the healthcare industry with insights into its processes. This will allow them to add more value to the healthcare business.

Automation in Medical Records

Automating medical records is one of the core functions of RPA. By automating the handling of health records, healthcare providers can reduce the need for manual work. This eliminates unnecessary delays, increases efficiency, and ensures cost savings.

Robotic process automation is useful in creating and maintaining a patient’s medical record. This task usually requires a great deal of precision and attention to detail. That makes it prone to human error when done manually. In addition, RPA helps collect data from multiple sources, such as lab results, imaging results, and doctors’ notes.

This data can then be automatically organized and validated to ensure consistency throughout the patient’s record. As a result, healthcare institutions have more accurate and up-to-date information to improve patient care.

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One of the most common uses of RPA for patient records is data slicing and dicing. Data slicing and dicing is used to quickly and easily divide a large dataset into smaller, more manageable chunks. This is extremely useful in the health sector.

It allows healthcare professionals to quickly review important patient data and make decisions based on that data. For instance, if a doctor requires a patient’s medical data, they can quickly slice and dice the entire record to access the specific data needed.

Another way RPA is used in this case is to automate data transfer between various healthcare providers. For instance, if patients need to transfer their medical history records from one provider to another, RPA facilitates the process.

AGILE HEALTHCARE: HOW TO IMPLEMENT THE APPROACH

This saves time and reduces possible human errors. Additionally, since RPA can safely automate the process, it complies with HIPAA guidelines.

RPA can also send reminders or messages about upcoming appointments or follow-up tests. This frees up staffing resources for other tasks. Similarly, repetitive tasks like billing and claim processing can be automated with RPA. This reduces the amount of time it takes to process claims and speeds up payments for both patients and providers.

In a nutshell, RPA is proving to be a powerful tool for the successful management of different forms of records in the healthcare industry.

What are Three RPA Tools?

Robotic process automation tools are typically designed and configured for specific business processes. They help streamline these processes by significantly reducing the time required to complete tasks. Three popular RPA tools are Blue Prism, UiPath, and Automation Anywhere. Let’s briefly discuss each of them.

Blue Prism is a leading RPA software platform for automating business operations and processes. It enables users to easily build, deploy, manage, and scale their automated processes. This helps them to take full advantage of modern technologies like cloud computing and AI. Blue Prism processes easily integrate with different enterprise systems and can manage exceptions and process significant amounts of data. Besides providing accurate, reliable, and secure processes, Blue Prism offers fast and easy scalability to meet the needs of growing businesses.

KEY DIFFERENCES IN EHR VS. EMR VS. PHR: WHAT TO CHOOSE FOR YOUR HEALTHCARE ORGANIZATION

UiPath is an RPA platform that automates manual and monotonous tasks; UiPath comes with an intuitive user interface and a simple drag-and-drop feature. Other tool features include screen scraping, exception handling, and PDF data extraction. In addition, UiPath supports web, web service, and data-driven processes, enabling users to deploy bots quickly across all systems. UiPath also allows users to store data in secure, encrypted repositories to ensure compliance and version control.

Automation Anywhere is an AI-powered, enterprise-grade robotic process automation platform that enables businesses to automate processes fast and cost-effectively. It provides advanced tools for robotic process automation, data scraping, AI, analytics, and more. This software eliminates manual processing and enhances operational efficiency while reducing costs. In addition, Automation Anywhere provides a secure environment to store data and enables users to extract and obtain insights from the data. With its deep integration functions, Automation Anywhere is one of the most popular RPA tools in the market.

KEEP A PULSE ON EPIC APP ORCHARD AND HOW IT BENEFITS THE HEALTH SYSTEMS

What is an Example of RPA?

The insurance industry is a great candidate for the use of RPA. This is due to the large amount of data that must be processed to manage policies effectively. A practical example of RPA use in the insurance industry is processing new policy notices.

When a customer signs up for an insurance policy, an RPA system scans documents and extracts important data. The data is then entered into the insurer’s systems. This intelligent document processing can reduce the time it takes for a new policy notice to be fully processed. It also reduces the chances of errors in inputting data. Furthermore, RPA applications are used for insurance claims management and verifying policy information. As a result, RPA automation significantly improves the customer experience in many industries.

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Robotic Process Automation (RPA): 6 Use Cases for Healthcare Providers

RPA has many potential use cases in healthcare to improve efficiency and automate manual tasks. Below are some of the major use cases of this powerful artificial intelligence tool.

RPA is used for electronic medical record (EMR) data entry to simplify the process of collecting and transforming the data of patients into consistent formats. This facilitates faster healthcare delivery and reduces the use of paper documents and files.

RPA use helps verify claims data and ensure smooth and timely processing of payments.

RPA is used to facilitate patient scheduling automatically. This makes appointments with a doctor and follow-up communications with patients efficient.

RPA bots can automate the process of identifying and ordering necessary medical supplies while monitoring inventory levels.

RPA is used to send out billing notifications, follow up on patient accounts, and collect payments.

RPA automatically checks for and monitors compliance with industry regulations and standards within the medical field.

HEALTHCARE APPS DEVELOPMENT: TYPES, EXAMPLES, AND FEATURES

How RPA works in Healthcare

Image Source

The core purpose of Robotic Process Automation (RPA) in healthcare is to streamline the most mundane and labor-intensive tasks.

RPA works by utilizing bots to simulate the actions of human workers on computer systems. Such activities include forward and backward movements of a mouse, data entry, and requests for specific applications.

The software bots are typically accessed and managed through graphical user interfaces. This means they don’t need programming proficiency to be executed or controlled. Instead, RPA bots replicate the same data manipulation, interaction with user interfaces, and data exchange with other digital systems as a human user would do.

The use of RPA technology for healthcare services is expected to grow with the implementation of new healthcare regulations. The regulations are intended to reduce mistakes and speed up the EHR process by automating manual activities that need human workers.

PREDICTIVE ANALYTICS VS. MACHINE LEARNING: WHAT IS THE DIFFERENCE

Healthcare professionals, however, face unique challenges in implementing RPA. Medical processes and data are complex and highly sensitive. This means providers must have the right strategy, technology, and expertise to comply with regulatory and security standards. In addition, intelligent automation processes must be customized for each healthcare organization within the healthcare industry.

The most common areas for implementing RPA include clinical operations and administrative services. For example, clinical operations tasks such as laboratory testing and insurance verification can be automated using RPA. By automating these processes, healthcare providers can reduce the time spent and effort used by their staff. And also reduce errors in results.

Data Mining Vs. Predictive Analytics: Know The Difference

RPA also provides opportunities for healthcare providers to improve the patient experience. For instance, by automating the scheduling and registration processes, patients will have a leisurely experience signing up for appointments and checking in. In addition, RPA can ensure more accuracy of medical information and reduce the risk of errors when data entry is done manually.

In summary, RPA can help healthcare organizations to simplify their workflow, reduce costs, and improve the patient experience. With the right strategy and implementation, healthcare providers can realize the benefits of RPA and reap the rewards from improved patient outcomes.

Benefits of RPA to Healthcare Organizations

Image Source

Here are some benefits healthcare organizations can enjoy by using RPA:

RPA can reduce costs by eliminating manual labor and delivering more incredible accuracy. In addition, it removes mistakes caused by manual data entry.

RPA allows for improved tracking and tracing of workflow in the healthcare system. This will enable better data sharing and records management.

RPA brings efficiency to the decision-making process. It improves data analysis by notifying decision-makers of any changes or the emergence of new data in real-time.

By automating workflows, organizations can devote more time to patient care. This delivers a higher quality of service and satisfaction.Streamlined Medical Claims

RPA can automate medical claims processing, significantly reducing errors and decreasing processing time.

Automating processes lowers the risk of data breach and fraud through greater security and enforcement of data protection protocols.

Automating specific processes can speed up ensuring compliance, improving security, and reducing risks.

Automated healthcare processes can reduce labor costs by replacing manual labor with more efficient, cost-effective tasks.

The intelligent automation provided by RPA can deliver a better overall experience for patients. It does this by accurately updating medical information and responding on time.

THE APP SOLUTIONS – CUSTOM HEALTHCARE SOFTWARE DEVELOPMENT COMPANY

Conclusion

RPA has the potential to significantly improve the efficiency and accuracy of many tasks in the healthcare industry. As a result, RPA can help healthcare professionals deliver high-quality patient care by taking over repetitive, time-consuming tasks. As the technology continues to advance, we can expect to see even more applications of RPA in healthcare, leading to better, more efficient care for all.

The healthcare industry is constantly evolving to meet its customers’ needs. A noteworthy trend that is emerging is the use of chatbots. These computer programs, which use artificial intelligence to automate customer service, make it easier for medical providers and patients to communicate.

Chatbots in healthcare are gaining traction, and research suggests that by 2032, the global market for healthcare chatbots will be worth $944.65 billion. The increase in internet penetration, smart device adoption, and the demand for remote medical assistance drive this market forward.

In this article, we’ll cover the three main types of healthcare chatbots, how they are used, their advantages and disadvantages, and which one is right for your organization.

BEST CHATBOT DEVELOPMENT TRENDS AND BUSINESS APPLICATIONS

Primary Categories of Medical Chatbots

Chatbots can be broadly divided into three main categories: clinical support, decision support, and healthcare. Let’s take a closer look at each one.

• Decision-support chatbots provide medical advice based on the data collected from the patient. They can be used to remind patients of drug interactions, suggested doses, and so on.
• Clinical support chatbots are developed to offer professional medical advice to doctors, helping them make more accurate diagnoses and treatment plans.
• Healthcare-focused chatbots are used to promote communication between health providers and patients. These chatbots are commonly employed in healthcare to respond quickly to common queries and provide general medical advice.

How Exactly are AI Chatbots being used in Healthcare?

Chatbots, powered by artificial intelligence, are used in various ways to improve the patient experience and simplify medical procedures. To get a better handle on the application of AI bots in healthcare, check out these examples: 

• Appointment Booking: 

Chatbots can be integrated with online booking systems, making it a cinch for patients to set up or change visits with their medics. 

• Virtual Health Guides: 

Chatbots use natural language processing (NLP) to comprehend and answer patient queries. For example, they can give information on common medical conditions and symptoms and even link to electronic health records so people can access their health information.

WHAT IS THE BEST WAY TO CREATE A CHATBOT: PLATFORM VS. CUSTOM 

• Clinical Studies: 

AI chatbots can assess patients for clinical trial eligibility and supply information about ongoing trials, accelerating the process of enrolling participants and collecting data.

• Prescription Refills:

Chatbots make it quicker than ever to get refills on prescriptions – no more waiting around.

• Mental Health:

Chatbots specially designed for mental health are invaluable for those struggling with depression, anxiety, and other issues. They provide a secure outlet for communication and lessen feelings of loneliness.

• Remote Monitoring:

Thanks to AI chatbot healthcare, remote patient health status monitoring is easier than ever. In addition, wearable devices can now supply data to healthcare providers to keep tabs on potential problems.

HOW MENTAL HEALTH CHATBOTS HANDLE STRESS?

It’s important to note that chatbots are never meant to supplant healthcare professionals – they make their jobs more straightforward and accessible to patients.

 

The Role of Intelligent Chatbots in Healthcare [2023 New Applications]

Health organizations are increasingly turning to chatbots, and this tendency will continue to gain momentum in 2023 and beyond. Some of the novel and creative approaches include the following:

Making a splash in the world of telemedicine is one of the most promising areas of application. Healthcare chatbots provide patients with virtual medical consultations and advice so they can avoid leaving the coziness of their homes to get professional assistance.

Chatbots can also be handy in managing and administering medication. These bots can remind patients to take their meds, give info regarding drug interactions, and alert them if there are any issues with their treatment.

5 CHALLENGES OF CHATBOTS FOR BUSINESS AND HOW TO OVERCOME THEM

Medical data analysis is another area where chatbots can prove useful. AI bots assist physicians in quickly processing vast amounts of patient data, enabling healthcare workers to acquire info about potential health issues and receive personalized care plans.

A healthcare chatbot can link patients and trials according to their health data and demographics, boosting clinical trial participation and accelerating research.

Chatbots can manage mundane tasks like scheduling appointments and providing simple answers about treatments and insurance.

The medical chatbot can assist as an interpreter for non-English speaking patients. The bot can then interpret during consultations and appointments, eliminating language issues.

AI chatbots are also being used to uphold and teach people about their well-being. It will give advice on healthy eating, offer lifestyle modifications, and remind them of other important activities.

Suicides are a growing epidemic, so let’s tackle it head-on with technology. We can design an app and chatbot with mental health resources that deliver tailored Cognitive Behavioral Therapy. AI tech can help those in need by reminding them of appointments, offering tips for treatment, and providing invaluable assistance in tackling their mental health issues.

BENEFITS OF USING CHATBOTS FOR YOUR BUSINESS

The Pros and Cons of Healthcare Chatbots

There are benefits and drawbacks to using chatbots in medicine, just as with any new technology. So why don’t we briefly talk about some of them below?

According to Statista, by 2022, the market size of customer service from artificial intelligence chatbots in China will amount to around 7.1 billion Yuan. AI can be a real “plus” for the healthcare industry too. 

Some of the many rewards it offers include:

Chatbots can help the health sector save an estimated $11 billion annually! Automating some tasks and quickly responding to basic questions result in reduced medical service expenses and free up doctors to tackle more complex issues.

Chatbots can be used to streamline and make healthcare services more efficient.

In addition to saving money, medical bots can offer faster access to healthcare services. According to a survey, 78% of people prefer using bots for medical services. 

AI-powered chatbots are able to provide comprehensive support and advice to patients and follow-up services.

Harnessing AI capabilities, chatbots can provide thorough aid and counsel to patients, as well as follow-up consultations and treatments.

THE HEALTHCARE ECOSYSTEMS: A TOUR THROUGH THE HEALTH-INTERCONNECTED WORLD 

A further benefit of a medical chatbot is that it can furnish individualized healthcare services, guidance, and assistance to patients. Utilizing the power of AI, these chatbots can provide every patient with personalized advice and reminders tailored to their requirements.

On the opposite side of the coin, there are a few obstacles to consider when contemplating the development of healthcare chatbots. Let’s take a gander at the downsides. 

Putting together an AI that can handle delicate medical information can be pretty intricate and take longer than expected.

One major disadvantage is that, for the time being, chatbots cannot deliver thorough medical counsel. Thus, these should be employed in conjunction with the direction of certified medical experts and not as a substitution.

Also, ethical and security problems may appear when bots access patient records. Some chatbots may not include the necessary safety measures to securely store and process confidential patient data, thereby risking patient privacy. Health services that employ a chatbot for medical reasons must take precautions to prevent data breaches.

TOP 10 HEALTHCARE TRENDS EXPECTED TO BE IN 2023

The stellar performance of healthcare chatbots is only as good as the info they’re fed. Feed them incorrect details, and their misdiagnoses or shady treatments flummox you. To ensure accurate results, keep patient data up-to-date and current!

Chatbots may not be able to provide the full scope of mental health support, so healthcare organizations must pair them with dedicated medical professionals for comprehensive aid.

How to Choose an AI Chatbot for Your Healthcare Organization

When choosing an AI chatbot for your healthcare organization, there are several factors to consider.

• Type

The first step in developing a healthcare chatbot is determining its purpose. Specifically, do you need one that can help you make decisions and support you clinically or one that focuses on providing general medical guidance to patients?

• Features 

Think about what the chatbot can do and what features it has. Askings questions like “can I get specific recommendations and reminders from the chatbot?” “Can patient information be safely stored and processed?” can help you make the right choice.

HOW MUCH DOES IT COST TO DEVELOP AN APP: DETAILED FEATURE BREAKDOWN

• Costs

Also, take into account the cost of the chatbot. They can be expensive, so you should consider the price and make sure it fits your budget.

• Security

Pay close attention to the chatbot’s security settings and how to protect patient data is essential. Ensure that it has the right security measures to keep sensitive patient information from getting into the wrong hands.

WHAT IS ARTIFICIAL INTELLIGENCE IN HEALTHCARE?

Chatbots and Their Place in Healthcare

Chatbots could help improve health care by providing information, answering patients’ questions, and helping to sort out symptoms. A chatbot can tell you about general health or how to deal with a certain condition, for example. They also help healthcare providers by answering patients’ frequently asked questions and directing them to the right care. 

Healthcare facilities must use chatbots in a responsible and protected manner. They can’t replace doctors and nurses, so that’s something to remember. For the best results in patient care, hospitals, clinics, and other organizations should integrate bots with medical professionals and psychologists.

HEALTHCARE APPS DEVELOPMENT: TYPES, EXAMPLES, AND FEATURES

Outcomes

Healthcare chatbots have the potential to revolutionize the health industry. They are a powerful and cost-effective way to provide medical advice and support to patients and health providers. They also provide personalized advice and reminders tailored to the individual patient’s needs.  

Technology is still in its early stages, and chatbots still need to be built, tested, and regulated based on their usage in medical care. It is important, though, that healthcare organizations use these bots safely and responsibly. Nevertheless, we are excited about the future!

Do you need a team of specialists who will work with you to create a healthcare chatbot for your app and protect against cyberattacks?

The APP Solutions is a leading healthcare technology company that creates innovative products to improve patient outcomes and streamline healthcare processes. Our talented developers and designers work hard to give our clients the most advanced, secure, and effective solutions to improve patient outcomes and streamline healthcare processes. 

We have a proven track record of delivering high-quality, user-friendly, and scalable healthcare technology solutions. Our expertise includes developing electronic health records (EHR) systems, telemedicine platforms, patient portals, and chatbots for mobile health, among other things. Our solutions are designed to comply fully with HIPAA and HITECH. Contact us today, and you will be glad you did.

Influencer marketing is expected to increase up to $10 billion by 2020, even though it is relatively a newcomer to the global business market.

Why is influencer marketing so promising that businesses are spending more and more money on it? It is a proven fact that 92% of people rely on an influencer’s opinion, following their recommendations in daily and business life. And it doesn’t matter whether they know these people or not – what’s said consistently is considered to be true. 

[Source: OpenInfluence]

There is no doubt: the influencer marketing works, but to make it really efficient, a need to find the right influencers who are relevant to your niche and speak directly to your target users is necessary. 

Searches can be either short or long, depending on the scale of business,  product, or service it’s providing, and whether these influencers are willing to promote them and work within the offered conditions. It’s more about being in the right place and talking to the right people. But what to do if it doesn’t work? How to find influencers who would be interested in your offering and bring new customers to your business?

What are the Influencer Marketing Platforms?

Basically, these platforms are the best place for finding influencers to work with. It’s a so-called catalog of people that offer their services in conducting marketing campaigns with both long and short term promotions. One can use filters and get the contacts of influencers suitable for a specific business case, and hire them. 

Influencer marketing campaigns are useful when considering cooperation with several influencers. Here it’s possible to find needed influencers in a few clicks on one platform instead of long searches on Google or gathering recommendations from partners. It’s also convenient for management purposes, as all communication and activities are conducted in the same place.

Why is Building AI-powered influencer marketing platforms More Beneficial?

Of course, there are dozens of influencer marketing platforms you can just come and use. However, every solution has its pros and cons. 

Such existing platforms save businesses’ time and effort and connect them with influencers. But, there are also some hidden pitfalls one should consider before starting to work with platforms.

It’s impossible to find ALL needed influencers on one platform

A platform is also a business, so it keeps a focus on specific media channels and offers a particular range of services. And, it isn’t a surprise that some channels are maintained on a more advanced level, some could work better. So, you won’t likely find that one particular platform with which you’ll accomplish several goals at once.

Therefore, only cooperation with two or more platforms will give the desirable marketing result and increase profits.

Famous influencers can be reached only via their managers

Your search results can become a waste of time and money if you want to involve celebrities in your business promotion. The most popular industry experts might not even be in a list of influencers represented on a particular platform. They are mostly working through talent managers.

No personal approach

In general, a platform has its marketing vision, content development strategy, and approach to work. This means you will always be dependent on its brand voice, tactics, and methods of engagement with the target audience. For some businesses, it can work, but there are no guarantees it will work for you as well. 

Moreover, the influencer marketing platform is still a new technology in the world, so certain issues can appear. The fact that you’re working with marketers doesn’t save you from the risk of working with the wrong influencers. Marketers are just doing their job – providing you with marketing services while the result of it is your responsibility. They can’t ensure a complete demographic targeting and timed messaging release. Even 61% of marketers consider this as a serious challenge for them.

In this case, the development of your influencer marketing platform, with Artificial Intelligence technologies involved, seems more beneficial than using an existing one.

What AI brings businesses in terms of influencer marketing platform development?

AI is considered to be a smart technology, and this is proved by the way it boosts influencer marketing in three key operations:

• Selection of right influencers
• Suggestions on business workflow
• Creation and filling content

Artificial Intelligence provides businesses with that level of precision and scalability that they cannot achieve, even while working with the best professionals. Technology can often offer more than a person. Of course, AI cannot replace humans in influencer marketing as this market is all about the interaction between humans. But when automation of processes is needed, AI can definitely enable this and allow free time for thinking of ways to provide a more exciting experience for users. 

On the platforms, you can find 1000+ influencers, therefore, imagine how much time you would need to learn each profile, and read references in order to come up with the right choice. Our guess is you will probably waste a lot of time. 

The above procedure makes searching very frustrating, and businesses need to redefine specific content in order to beat the masses. AI can save you from this inefficient process and help find the right match in a more fast and efficient way.

You won’t find any better mechanism that will help you to define the right influencer for your marketing campaign. It’s the perfect match of technology in the way it reads and understands the needs of a specific business. 

Stages of AI-powered Influencer Marketing Platform Development

Let’s discover what stages of AI-powered influencer marketing platform development you can expect if you decide to build it from scratch.

Include the following AI technologies

We will start with  must-have technologies for a proper influencer marketing platform to function:

Textual Content and Image Recognition

The technology of NLP AI can analyze the content and generate search engines based on interests, industries and demographics, to provide you with relevant answers regarding requests.

Analytics mechanism about Audience Interaction

AI can provide you with an analysis of the users’ reactions to each particular post. Moreover, it can gather this data and apply metrics obtained to improve  statistics based on feedback. This technology is to insure the constant improvement of your content. 

Creator of relevant suggestions

 On the platforms, you can find 1000+ influencers, therefore, imagine how much time you would need to learn each profile, and  read references in order to come up with the right choice. Our guess is you will probably waste a lot of time.

[Source: OpenInfluence]

Moreover, the market is overloaded with promotional ads on brands that make searching more annoying, therefore businesses need to redefine content in order to beat the masses. So, this technology has to be included in the list as it will save you from this useless process and help to find the right match in a more fast and efficient way.

Detection of fakes and possible frauds

The more profitable the market becomes, the more fraudulent influencers become interested in it. Social bots are increasingly attacking the influencer market which caused the industry a loss of $1.3 billion in 2019. Therefore. it is vital to include pattern/footprint analysis to distinguish possible fraud profiles to automatically avoid huge money losses. The usual marketing platforms are useless in this case as only  AI-powered features can enable high-level online protection.

Define your Type of Content

Today’s market is overloaded with promotional ads – around 95 million new photos appear on Instagram every single day. This makes the promotion of your content quite challenging and forces you to find the ways/tools/technologies to cut your business content through the noise of your competitors to attract your users’ attention. 

Here it is vital to make sure that your content is really relevant and useful for users. That might sound very simple and annoying to read in different articles – but you won’t find any other solution to your content success.

Artificial Intelligence can boost your content efficiency. We advise you to use Artificial Neural Networks (ANN) to analyze, in a few seconds, trillions of images and videos that are uploaded to social platforms. This tool can help define the attributes, characteristics, types of images, and content that will appeal to your target audience to be used in your content strategy.

Also, NLP analyzes comments and determines which posts receive the most significant feedback from users.

All of the above gives you the necessary insights on how to develop your content, promote, analyze, and apply these metrics to continually improve your content.

Measure, Analyze, and Apply

Collect and analyze engagement metrics from your social business profiles to increase your revenue.

Artificial Intelligence enables regression analysis of critical analytics data with algorithms and allows one to use these models to predict future performance. 

The next way to predict ROI is benchmarking and forecasts. It can help with dealing with the continually increasing flow of Big Data and inputs.

Develop the Influencer Marketing Platform with the APP Solutions

The company APP Solutions knows how to develop an influencer marketing platform and empower it with Artificial Intelligence. Our experience and expertise enable us to define really efficient marketing tools to provide you with the results you expect to achieve with an influencer marketing platform.

This technical structure is a guide on how to build an AI-powered influencer marketing platform.

So what’s required?

• Architecture, project setup
• Design
• Social media collector
• Rewarding point system
• API that shows news from third party news hubs
• Static page
• Report abuse
• Subscription model
• Payment system integration
• Survey tool  – third party API
• Automatic sharing for posts on social media platforms
• User/follower profile page
• Influencer interface
• Advertiser interface
• Admin panel

If you need assistance or professional advice on any of these components or want to build all of them, the APP Solutions is always glad to hear from you.

Final Thoughts

The pace at which the influencer market is moving in 2020 has impressed both startups and established businesses. While the market offers a variety of influencer marketing platforms to choose from for cooperation, it’s still more beneficial to custom an AI-powered influencer marketing platform. Artificial Intelligence provides automated and smooth processes while preventing businesses from wasting time and money. The APP Solutions believes that today, relying on the schedule and principles of third-party platforms is not as advantageous as building your own platform. Moreover, you always have us – a partner you can contact to receive all the necessary advice.

Related reading:

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COMPUTER VISION FOR HEALTHCARE

Healthcare and data science are something of a perfect pair. Healthcare operations require insights into patient data to function at a practical level. At the same time, data science is all about getting deep into data and finding all sorts of interesting things. 

The combination of these two resulted in the adoption of Electronic Health Records (EHR) that use a data science toolkit for the benefit of medical procedures.

In addition to this, healthcare is the perfect material for various machine learning algorithms to streamline workflows, modernize database maintenance, and increase the accuracy of results.

In this article, we will explain what EHR is and how machine learning makes it more effective.

What is EHR?

Electronic Health Record (aka EHR) is a digital compendium of all available patient data gathered into one database. 

The information in EHR includes medical history, treatment record data such as diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results.

• The adoption of EHR in the industry kickstarted in the late 90s after the enacting and signing of HIPAA (Health Insurance Portability and Accountability Act) in 1996. 
• However, due to technological limitations, things proceeded slowly. 
• The technology received a significant boost after the passing of the HITECH (Health Information Technology for Economic and Clinical Health) Act in 2014 which specified the whats, whys, and hows of EHR implementation.

The main goal of implementing EHR is to expand the view of patient care and increase the efficiency of treatment.

HOW TO MAKE EHR/EMR EPIC INTEGRATION WITH YOUR HEALTH APP

In essence, EHR is like a good old patient’s paper chart which expands into a full-blown, interactive, data science dashboard, with real-time updates where you can examine the information and also perform various analytical operations. 

• Think about it as a sort of Google Account type of thing, where your data is gathered into one place and you can use it for multiple purposes with tools like Office 365 or the likes.

The critical characteristics of Electronic Health Records are:

1. Availability – EHR data is organized and updated in real-time for further data science operations, such as diagnostics, descriptive analytics, predictive analytics, and, in some cases, even prescriptive analytics. It is available at all times and shared with all required parties involved in a patient’s care – such as laboratories, specialists, medical imaging labs, pharmacies, emergency facilities, etc. 
2. Security – the information is accessed and transformed by authorized users. All patient data is stored securely by extensive access management protocols, encryption, anonymization, and data loss protection routines.
3. Workflow optimization – EHR features can automate such routine procedures as recurrent Automate and streamline provider workflow. In addition to this, EHR automation can handle healthcare data processing regulations such as HITECH, HIPAA (USA), and PIPEDA (Canada) by implementing required protocols during data processing.

Electronic Health Records vs. Electronic Medical Record – What’s the Difference?

There is also another type of electronic record system used in healthcare operations – Electronic Medical Records AKA EMR. 

The main difference between EHR and EMR is the focus on different persons involved in medical procedures. 

• EMR is a digital version of the dataflow in the clinician’s office. It revolves around a specific medical professional and contains treatment data of numerous patients within the specialist’s practice.
• In contrast, EHR data revolves around the specific patient and his medical history. 

In one way or another, EHR intertwines with numerous Electronic Medical Records within its workflow. There is a turnaround of data going back and forth – medical histories, examination data, test results, time-based data comparison, and so on.

Read a more detailed overview of EHR/EMR differences in the article EHR, EMR and PHR Differences

How AI/ML fits into Electronic Health Record?

As was previously mentioned, the availability of data is one of the primary benefits of implementing Electronic Health Records into medical proceedings. 

Aside from data being available for medical professionals at all times, the way medical data features in EHR makes it perfectly fitting for various machine learning-fueled data science operations.

WHAT IS AI IN HEALTHCARE?

Overall, machine learning is a viable option in the following aspects of Electronic Health Record:

• Data Mining
• Natural Language Processing 
• Medical Transcription
• Document Search
• Data Analytics
• Data Visualization
• Predictive Analytics
• Privacy and regulatory compliance

Let’s look at them one by one.

Data mining 

Gathering valuable insights is one of the essential requirements for providing efficient medical treatment. One of the challenges that come with gaining insights is that, in order to do that, you need to go through a lot of data. This process takes a lot of time.

With the increasing scope of data generated by medical facilities and its growing complexity – the use of machine learning algorithms to process and analyze information during data mining becomes a necessity. 

Overall, the use cases for Data mining in Electronic Health Record revolve around two approaches with different scopes:

• Finding data about the patient and his treatment. In this case, ML is used to round up relevant information in the medical history and treatment record to assist further in the decision-making process. 
• On the other hand, patient-centered data mining is used to assess different types of treatment and outcomes by studying similar cases from the broader EHR database.
• Data extraction for medical research across multiple EHR/EMR, and also public health datasets. In this case, a machine learning application is used to gather relevant data based on specific terms and outcomes across the EHR database. For example, to determine which types of medication for particular ailments were proven to be active and under what circumstances.
• On the other hand, the same tools apply for exploratory research that reshapes available data according to specific requirements — for example, examining test result patterns of annual lipid profiles.

DATA MINING VS. PREDICTIVE ANALYTICS: KNOW THE DIFFERENCE

Predictive Analytics

EHR is all about data analytics and making it more efficient. One of the most important innovations brought by Electronic Health Record is streamlining the data pipeline for further transformations.

The thing is – EHR machine learning-fueled data processing provides a foundation to identify patterns and detect certain tendencies occurring throughout numerous tests and examinations of a specific patient across multiple health records. 

• With all patient data and respective reference databases intertwined into a single sprawling system – one can leverage the available data to predict possible outcomes based on existing data. 
• Predictive analytics assist the doctor’s decision-making process by providing more options while considering possible courses of action.
• On the other hand, machine learning predictive analytics reduces the time required to pro.  

Predictive analytics models are trained case-by-case on the EHR databases. The accumulation of diverse data allows them to identify common patterns and outliers regarding certain aspects of disease development or a patient’s reaction to different treatment methods.

Let’s take DNA Nanopore Sequencing as an example. 

• The system combines input data (coming from the patient) with data about the illness and ways of treating it. 
• The predictive algorithm determines whether a particular match of treatment will result in a positive outcome and to which extent. (you can read more about Nanostream in our case study).

Natural Language Processing

In one way or another, natural language processing is involved in the majority of EHR-related operations. The reason for that is simple: most medical record documentation is in a textual form combined with different graphs and charts to illustrate points.

• Why not use a simple text search instead? Well, while the structure of the document is more or less uniform across the field, the manner of presentation may vary from specialist to specialist. NLP solution provides more flexibility in that regard.

The main NLP use cases for Electronic Health Record are the following:

• Document Search – both as part of the broader data mining operation and simply as an internal navigation tool. In this case, the system uses a named-entity recognition model trained on a set of specific terms and designations related to different types of tests and examinations. As a result, doctors can save time on finding relevant information in the vast scopes of data. Depending on the purpose, the search results form via the following methods:
• By context – locating information within the document – vanilla document search. For example, you can perform a comparison of physical examination reports criteria by criteria.
• Terms/Topics/Phrases – extracting instances of specific terms used or topics mentioned. For example, a doctor can obtain all blood test results and put them into perspective.
• Search across multiple documents;
• One of the most prominent current applications is the Linguamatics I2E platform which also provides data visualization features.
• Medical transcription – in this case, NLP is used to recognize speech, and subsequently, format it in an appropriate way (for example, break down into segments by context).
• The speech-to-text component operates with a set of commands like “new line” or “new paragraph.”
• Nuance Communications make one of the most prominent products of this category. Their tools, Nuance Dragon, augments EHR with a conversational interface that assists with filling data into the record.

• Report generation – in this case, NLP functions as a form of data visualization in a textual form. These models are trained on existing reports and operate on specific templates (for example, for blood test results). Due to the highly formalized language of the reports, it is relatively easy to train a generative model based on term and phrase collocation and correlation. 
• In this case, the correct verbiage is analyzed out of the habitual juxtaposition of a particular word with another word or words with a frequency higher than chance (collocation) and the extent to which two or more variables fluctuate together (correlation). 

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Data Visualization

Data visualization is another important aspect of data analytics brought to its full extent with the implementation of Electronic Health Records. 

Visualization is one of the critical components that make Electronic Health Record more effective in terms of accessibility and availability of data for various data science operations. 

• The thing is – as an electronic health record is basically a giant graph with lots of raw data regarding different aspects of the patient’s state, as such, it is not practical to use it in this state. The role of visualization, in this case, is to make data more accessible and understandable for everyday purposes. That has to be obvious, right?

However, you can’t use the same data visualization template for every EHR. While the framework remains the same, it requires room for customization to visualize patient data on the EHR dashboard adequately. 

The role of machine learning in this operation parallels its role in data mining. However, in the case of data visualization, it is about interpreting data in an accessible form. 

At the current moment, one of the most frequently used visualization libraries in Electronic Health Record is d3. For example, we have used its sunburst and pie charts in the Nanostream project. 

HEALTHCARE CYBERSECURITY: HOW TO PROTECT PATIENT DATA

Regulatory compliance, privacy, and patient data confidentiality

Healthcare is an industry that mostly operates with sensitive data through and through. Pretty much every element of healthcare operation, in one way or another, touches certain aspects of privacy and confidentiality. 

The fact is that integrated systems like EHR are vulnerable to breaches, data loss, and other unfortunate things that may happen to data in the digital realm. 

In addition to that, healthcare proceedings are bound by government regulations that detail the ins and outs of personal data gathering, processing, and storing in general, and specifically in the context of healthcare.

Such regulations as the European Union’s GDPR, Canada’s PIPEDA, and United States’ HIPAA describe how to handle sensitive personal data and what the consequences are of its mishandling.

The implementation of EHR makes compliance with these regulations much more convenient as it allows us to automate much of the compliance workflow. Here’s how:

• Anonymization during data processing – in this case, patient data is prepared for testing, but non-crucial identifiable elements, such as names, are concealed.
• Access management – EHR structure allows limiting access to patient data only for those involved in a patient’s treatment. 
• A combination of encryption for data-at-rest and data-in-transit – the goal is to avoid any outside interference into data processing.

THE APP SOLUTIONS – CUSTOM HEALTHCARE SOFTWARE DEVELOPMENT COMPANY

In Conclusion

The adoption of electronic health records and the implementation of machine learning elevates healthcare operations to a new level.

On the one hand, it expands the view on patient data and puts it into the broader context of healthcare proceedings.

On the other hand, machine learning-fueled EHR provides doctors with a much more efficient and transparent framework for data science that results in more accurate data and deeper insights into it.

What our clients say 

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Healthcare is an industry permanently aimed at future technologies. It is one of those sectors eager to embrace emerging tech to see if it can make a difference in its quest to cure diseases and save people’s lives. 

Given the fact that healthcare proceedings are data-heavy by design, it seemed evident that sooner than later machine learning, in all its variety, would find its way to the healthcare industry. 

In that context, medical imaging is one of the most prominent examples of effective deep learning implementation in healthcare operations.

In this article, we will:

• Explain the basics of medical imaging;
• Explain how deep learning makes medical imaging more accurate and useful;
• Describe primary machine learning medical imaging use cases;

What is medical imaging?

The term “medical imaging” (aka “medical image analysis”) is used to describe a wide variety of techniques and processes that create a visualization of the body’s interior in general, and also specific organs or tissues. 

Overall, medical imaging covers such disciplines as:

• X-ray radiography;
• magnetic resonance imaging (MRI);
• ultrasound;
• endoscopy; 
• thermography; 
• medical photography in general and a lot more.

The main goal of medical image analysis is to increase the efficiency of clinical examination and medical intervention – in other words, to look underneath the skin and bone right into the internal organs and discover what’s wrong with them.

• On the one hand, medical imaging explores the anatomy and physical inner-workings. 
• On the other hand, medical image analysis helps to identify abnormalities and understand their causes and impact. 

With that out of the way, let’s look at how machine learning and deep learning, in particular, can make medical imaging more efficient.

Why deep learning is beneficial for medical imaging?

One of the defining features of modern healthcare operation is that it generates immense amounts of data related to a variety of intertwined processes. Amongst different healthcare fields, medical images generate the highest volume of data. And, it grows exponentially because the tools are getting better at capturing data. 

Deep inside that data are valuable insights regarding patient condition, the development of the disease/anomaly, and the progress of the treatment. Each piece contributes to the whole and, it is critical to put it all together into a big picture as accurately as possible. 

However, the scope of data often surpasses the possibilities of traditional analysis. Doctors can’t take into consideration so much data. 

This aspect is a significant problem given the fact that data Interpretation is one of the most crucial factors in such fields as medical image analysis. The other issue with human interpretation is that it is limited and prone to errors due to various factors (including stress, lack of context, and lack of expertise). 

Because of this, deep learning is a natural solution to the problem.

Deep learning applications can process data and extract valuable insights at higher speeds with much more accuracy. This can help doctors to process data and analyze test results more thoroughly. 

The thing is – with that much data at hand, the training of deep learning models is not a big challenge. On the other hand, the implementation of deep learning in healthcare proceedings is an effective way to increase the efficiency of operation and accuracy of results. 

The primary type of deep learning application for medical image analysis is a convolutional neural network (you can read more about them here). CNN uses multiple filters and pooling to recognize and extract different features out of input data. 

How deep learning fits into medical imaging?

The implementation of deep learning into medical image analysis can improve on the main requirements for the proceedings. Here is how: 

• Provide high accuracy image processing; 
• Enable input image analysis with an appropriate level of sensibility to certain field-specific aspects (depends on the use case. For example, bone fracture analysis).

Let’s break it down in an understandable term example, an x-ray of bones: 

• Shallow layers identify broad elements of an input image. In this case – bones. 
• Deeper layers identify specific aspects – like fractures, their positions, severity, and so now. 

The primary operations handled by deep learning medical imaging applications are as follows:

• Diagnostic image classification – involves the processing of examination images, comparison of different samples. It is primarily used to identify objects and lesions into specific classes based on local and global information about the object’s appearance and location.
• Anatomical object localization – includes localization of organs or lesions. The process often involves 3D parsing of an image with the conversion of three-dimensional space into two-dimensional orthogonal planes. 
• Organ/substructure segmentation – involves identifying a set of pixels that define contour or object of interest. This process allows quantitative analysis related to shape, size, and volume.
• Lesion segmentation – combines object detection and organ / substructure segmentation.  
• Spatial alignment – involves the transformation of coordinates from one sample to another. It is mainly used in clinical research.
• Content-based image retrieval – used for data retrieval and knowledge discovery in large databases. One of the critical tools for navigation in numerous case histories and understanding of rare disorders.
• Image generation and enhancement – involves image quality improvement, image normalizing (aka cleaning from noise), data completion, and pattern discovery. 
• Image data and report combination. This case is twofold. On the one hand, report data is used to improve image classification accuracy. On the other hand, image classification data is then further described in text reports.

Now let’s look at how medical image analysis uses deep learning applications.

Deep Learning in Medical Imaging Examples

Deep learning cancer detection

At the time of writing this piece, cancer detection is one of the major applications of deep learning CNNs. This particular use case makes the most out of deep learning implementation in terms of accuracy and speed of operation.

This aspect is a big deal because some forms of cancer, such as melanoma and breast cancer, have a higher degree of curability if diagnosed early. 

On the other hand, deep learning medical image analysis is practical at later stages. 

For instance, it is used to track and analyze the development of metastatic cancer. One of the most prominent deep learning models in this field is LYmph Node Assistant (LYNA) developed at MIT. The LYNA model trained on pathological slides datasets. The model reviews sample slides and recognizes characters of tumors and metastases in a short time span with a 99% rate of accuracy. 

In the case of skin cancer detection, deep learning is applied at the examination stage to identify anomalies and track its development. To do that, it compares sample data with available datasets such as T100000. (You can read more about it in our recent case study).

Breast cancer detection is the other critical use case. In this case, a deep learning neural network is used to compare mammogram images and identify abnormal or anomalous tissues across numerous samples.

Tracking tumor development

One of the most prominent features of convolutional neural networks is its ability to process images with numerous filters to extract as many valuable elements as possible. This feature comes in handy when it comes to tracking the development of the tumor.

One of the main requirements for tracking tumor development is to maintain the continuity of the process i.e., identifying various stages, transition points, and anomalies. 

The training of tumor development tracking CNN requires a relatively small number of clinical trials in comparison with other use cases. 

The resulting data reveal critical features of the tumor with various image classification algorithms. The features include tumor location, area, shape, and also density. 

In addition to that, such CNN can: 

• track the changes of the tumor over time; 
• tie this data with the impacting factors (for example, treatment or lack thereof). 

In this case, the system also uses predictive analytics to analyze tumor proliferation. One of the most common methods for this is tumor probability heatmap that classifies the state of the tumor based on the tissue patch overlap.

Deep learning medical image analysis – MRI image processing acceleration

MRI is one of the most complicated types of medical imaging. The operation is both resource-heavy and time-consuming (which is why it benefits so much from cloud computing). The data contains multiple layers and dimensions that require contextualization for accurate interpretation.

Enter deep learning. The implementation of the convolutional neural network can automate the image segmentation process and streamline its proceedings with a wide array of classification and segmentation algorithms that sift through data and extract as many things of note as required.

The operation of MRI scan alignment takes hours of computing time to complete. The process involves sorting millions of voxels (3D pixels) that constitute anatomical patterns. In addition to this, the same process is required for numerous patients time after time. 

Here’s how deep learning can make it easier. 

• Image classification and pattern recognition are two cases in which neural networks are at best. 
• The convolutional neural network can train to identify common anatomical patterns. The data goes through multiple CNN filters that sift through it and identify relevant patterns.
• As a result, CNN will be capable of spotting anomalies and identify specific indications of different diseases.

The segmentation process may involve 2d/3d convolution kernels that determine the segmentation patterns. 

• 2D CNN slices the data one-by-one to construct a pattern map;
• 3D CNN uses voxel data that predicts segmentation maps for volumetric patches.

As such, segmentation is a viable tool for diagnosis and treatment development purposes across multiple fields. In addition to that, it contributes significantly to quantitative studies and computational modeling, both of which are crucial in clinical research.

One of the most prominent implementations of this approach is MIT’s VoxelMorph. This system used several thousand different MRI brain scans as training material. This feature enables the system to identify common patterns of brain structure and also spot any anomalies or other suspicious differences from the norm.

Retinal blood vessel segmentation

Retinal blood vessel segmentation is one of the more onerous medical imaging tasks due to its scale. The thing is – blood vessels take just a couple of pixels contrasting with background pixels, which makes them hard to spot, not to mention analyze at the appropriate level.

Deep learning can make it much more manageable. However, it is one of the cases where deep learning takes more of an assisting role in the process. Such neural networks use Structured Analysis of the Retina, aka STARE dataset. This dataset contains 28 999×960 annotated images. 

Overall, there are two ways deep learning improves retinal blood vessel segmentation operation:

1. Image enhancement can improve the quality of an image.
2. Substructure segmentation can correctly identify the blood vessels and determine their state. 

As a result, the implementation of neural networks significantly compresses the time-span of workflow. The system can annotate the samples on its own as it already has the foundational points of reference. Because of that, the specialist can focus on the case-specific operations instead of manually reannotating samples every time. 

Deep learning cardiac assessment

Cardiac assessment for cardiovascular pathologies is one of the most complicated cases that require lots of data to spot the pattern and determine the severity of the problem. 

The other critical factor is time, as cardiovascular pathologies require swift reaction to avoid lethal outcomes and provide effective treatment. 

This is something deep learning can handle with ease. Here’s how. Deep learning fits into the following operations:

• Blood Flow Quantification – to measure rates and determine features.
• Perform anomaly detection in the accumulated quantitative data. 
• Data Visualization of the results. 

The implementation of deep learning in the process increases the accuracy of the analysis and allows doctors to gain much more insight in a shorter time. The speed of delivery can positively impact the course of treatment.

Musculoskeletal Radiograph’s Abnormality Detection 

Bone diseases and injuries are amongst the most common medical causes of severe, long-term pain and disability. As such, they are a high testing ground for various image classification and segmentation CNN use cases.

Let’s take the most common method of bone imaging – X-rays. While the interpretation of images is less of a problem in comparison with other fields, the workload in any given medical facility can be overwhelming for the resident radiologist. 

Enter deep learning: 

• CNN is used to classify images, determining their features (i.e., bone type, etc.) 
• After that, the system segments the abnormalities of the input image (for example, fractures, breaks, spurs, etc.).

As a result, the implementation of deep learning CNN can make the radiologist’s job more accessible and effective.

In Conclusion

From the data volume standpoint, medical image analysis is one of the biggest healthcare fields. This alone makes the implementation of machine learning solutions a logical decision. 

The combination is beneficial for both.

• On one hand, medical imaging gets a streamlined workflow with faster turnaround and higher accuracy of the analysis.
• On the other hand, such an application contributes to the overall development of neural network technologies and enables their further refinement.

What our clients say 

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Computer vision is a technology based on image processing and synthesis. It usually involves machine learning and allows AI to simulate human vision. This technology aims to save time by automating manual image analysis and achieving a higher level of accuracy. For this purpose, an algorithm is fed with a vast amount of representative pictures and trained to detect particular parts of them. Developers currently use computer vision in a wide range of spheres: from Snapchat and Instagram masks to scientific research and medical objectives. 

Main Reasons to Use Computer Vision in the Medical Field

Even though the state of the art computer vision does not enable technology to replace medical professionals, it does simplify their work. Here are some significant causes to start using computer vision in health care.

• Speed

If there is one main reason to use computers instead of people in specific processes, it is their ability to conduct calculations and analytical work faster. Paradoxical as it may seem, speed applies to medicare as well, along with other fields. People can process only a limited amount of information at a time. This is where AI takes over the monotonous and routine part of work.

With all the hardware, fast internet, and cloud we have nowadays, computers can process images in microseconds. This allows doctors to have AI analyze, let’s say, all the X-ray images while they can focus more on patients. Thus, doctors have a chance to find out more specific details through their soft skills and provide care to more of those who need it.

• Accuracy

Some diseases, like cancer, require early-stage diagnostics for doctors to save a human life. False-negative and false-positive results can both be rather destructive. In such cases, a patient either does not start treatment on time or make crucial decisions based on their knowledge about a presumed non-existent disease. Computer vision excludes the possibility of human error to some degree and serves as an assistant for radiologists. AI can help doctors detect such conditions as cancer, pneumonia, osteoporosis, and many others.

• Urgency

Healthcare presupposes many urgent situations that need an immediate reaction. Medical specialists are taught to estimate the situation visually very quickly in such cases. But what happens if they are not accurate enough? This is why a combination of speed and accuracy provided by computer vision might be crucial for urgent situations.

Automatic postpartum hemorrhage estimation is an example of the application of computer vision in the medical field. It allows surgeons to understand how much blood a patient has lost. The Triton system measures amounts of blood on used sponges and in canisters. The system helps doctors to decrease the number of maternal deaths and the duration of hospital stay.

• Pattern Recognition

Radiologists may also receive help from computers. You only need to compose a dataset of images with particularly associated diagnoses and train a deep learning model based on that dataset. Then, the AI will start detecting patterns in the images. For example, it can find tumors, pneumonia, or potentially dangerous moles.

Examples of Computer Vision Applications in Healthcare

Let’s have a closer look at the applications of computer vision in healthcare.

Skin Cancer Detection

“1 in 5 people get skin cancer”, according to the website of SkinVision — an app that helps the user detect skin cancer. You need to download, install the app, and take a photo of a mole or spot that concerns you. Then, the app will tell you whether you need to see a dermatologist or not. The software sensitivity is 95% due to the Machine Learning algorithm.

Surgery Simulations

Computer vision for medical imaging is also used for training. CV-empowered training especially applies to surgeons: nowadays, they can master their skills with digital models. The Touch Surgery software allows doctors to go through simulations of different surgeries. Artificial Intelligence creates interactive 3D models of human bodies, allowing surgeons to operate them almost the same as in real life.

Pneumonia Detection

A pneumonia detection web app is based on a neural network that was trained on 500 chest X-ray images. The deep learning model achieved 86%+ accuracy. Yet, it is an open-source project which can only be used for research purposes, not clinical ones.

Developers of another pneumonia detection instrument complain that finding labeled data is difficult because only certified doctors can give a diagnosis. They are also unsure that the result will be relevant for other conditions since their database was limited to 1–5-year-old patients from a single hospital.

[Pneumonia detection from chest radiograph using deep learning]

Our Expertise

The APP Solutions team built a skin cancer classification neural network. One of the project’s main challenges was time-sensitivity. While the system needs time to process data, the result should be visible as soon as possible. Another difficulty lies within the computational resources the product requires. Building the entire system on the cloud was a single solution. Additionally, it cut the cost of the project by half.

[Source]

Eventually, the product flow worked like this:

1. Input image uploaded to the Cloud Storage and sent to Convolutional Neural Network;
2. CNN processes input images;
3. Anomaly detection algorithm finds suspicious elements;
4. The classification algorithm determines the anomaly type;
5. Processing results saved to the database; 
6. The results summarized and visualized. 

The classifier takes into account the anatomic location of the lesion, patient profile data, lesion size, scale, and other characteristics.

As a result, we created a system with an average accuracy of 90% on testing data and a result delivery duration of one hour. The longer the system works, the more efficient it becomes.

Future of Computer Vision in Healthcare

Currently, there exists one big problem in healthcare computer vision. Deep learning models tend to not work well on new data (different from those they were trained on). So, there is still a whole lot to be done in the field.

Market predictions are showing the same: the peak of computer vision in healthcare is not here yet. CV in the healthcare market is predicted to grow at 47.2% and reach USD 1,457 million by 2023 compared to USD 210.5 million in 2018. 

Conclusion

Computer vision in healthcare has a lot to offer: it is already helping radiologists, surgeons, and other doctors. This technology can also partially substitute professional training for doctors and primary skin cancer screening. Tendencies and challenges show: a computer vision breakthrough in the medical field is yet to come. If you are interested in becoming a part of it, do not hesitate to contact us.

What our clients say

Read also: 

Benefits of Cloud Computing in the Healthcare Industry

Calmerry Telemedicine Platform Case Study