During this episode of our Careminds podcast, we discuss the complexities of product management and go-to-market strategies with our guest, Donna Cichani. Donna has a background in product management, A/B testing, and data analysis, and has worked with notable organizations such as Johns Hopkins Medicine, KPMG US, and JP Morgan. Currently, she is the lead product manager at Heal.
Our conversation with Donna covers topics like data analysis and strategic product planning, the differing mindsets between 0 to 1 and one to end product development, and methods to increase user engagement and product optimization. Drawing from her diverse experience in industries like healthcare, technology, banking, and finance, Donna shares her thoughts on the importance of strategic planning in product management.
Defining Success Criteria for Product Stages
When determining the success of a product, you consider both the user perspective and the business perspective. Using the example of an RPM solution called Pulse, designed for chronic disease management at Heal, we can explore the key performance indicators (KPIs) and metrics that matter most.
Firstly, there are patient-centric KPIs that focus on adoption and usage. Monitoring how often users engage with the solution to record their vitals and biometrics is crucial. The main goal is to encourage patients to stay proactive in managing their chronic conditions by using the solution more frequently.
User centricity is key, focusing on how you are improving life and the experience for the end user.
Secondly, clinical outcomes are also important. By tracking improvements in specific health measures, such as A1C levels for diabetic patients or maintaining healthy blood pressure ranges for hypertensive patients, we can gauge the effectiveness of the solution in promoting better health.
Also, business KPIs, such as attribution, play a significant role. For the RPM solution, it is important to know what percentage of patients using the solution are attributed to Heal for their primary care doctors.
Defining the best approach for optimizing a product depends on the specific product and its maturity curve. Take, for example, the RPM solution mentioned earlier. The primary goal of any RPM solution is to encourage users to engage with it consistently and measure their biometrics routinely.
At one point, the team behind the RPM solution considered expanding its features to include medication refill reminders, envisioning a more comprehensive ecosystem for patient monitoring. However, they quickly recognized the importance of perfecting their core RPM capabilities before adding secondary features. By maintaining focus on their core competency, they ensured they wouldn’t dilute the solution’s main purpose.
Optimization often involves considering the user experience, especially when it comes to healthcare solutions. In the case of the RPM solution, refining its core features contributed significantly to increased patient engagement. This example highlights the importance of prioritizing the optimization of a product’s primary functions before expanding its scope.
When to Focus on New Features or Enhancements in Product Development
You should invest heavily in user research as it’s crucial for driving customer adoption and engagement. During the discovery phase, our team spent considerable time observing patients in their natural environments, using existing products like glucometers, and capturing their day-to-day experiences. This research also included understanding how nurses, doctors, and other providers utilized data points during home visits.
By conducting ethnography studies, user research, and interviews, we were able to identify key pain points, which we then translated into enhancements and feature opportunities to drive engagement. To ensure customer adoption, it’s essential to focus on understanding users’ pain points, observe their interactions with your product or similar products, and avoid relying solely on secondary sources or high-level questions.
I don’t think that user research for usability testing ends during the discovery phase.
It’s important to note that user research and usability testing don’t end during the discovery phase. After creating our first prototype, we went through two additional rounds of usability testing to validate our assumptions, identify any flaws in our user flow, and refine the solution iteratively. This process continued up until the launch of the minimum viable product (MVP).
The ability of product managers to remain detached from their original plans, even after investing significant time and effort, is fascinating. When real data no longer supports the initial plan, it’s crucial to let it go, find a new direction, and create a better product that serves users more effectively. This adaptability is an essential aspect of successful product management.
Effective Optimization Techniques & The Best Ways to Apply Them
Optimization techniques focus on understanding existing processes, examining them through the lens of various stakeholders involved in the end-to-end flow, and identifying opportunities for efficiencies. For instance, by analyzing a process that takes 10 days and involves five stakeholders, you can uncover ways to reduce the number of stakeholders or the time each takes to complete their part.
Process mapping, a technique that visually represents the steps involved in a process, helps identify bottlenecks, redundancies, and areas for improvement. A/B testing is another valuable technique, where two different versions of a feature or product are tested with the target audience to determine which performs better.
In my experience, one of the keys to successful optimization is to involve the entire team in the process.
Involving the entire team, including product, engineering, design, sales, and marketing, leads to a more holistic view of challenges and opportunities, ultimately driving better optimization decisions. Keeping the end user in mind is crucial, as the goal is to enhance their experience.
It’s important to acknowledge that the rapid growth of product management as a career has led to a mix of undisputed go-to practices and those still being defined through trial and error. Sharing experiences and learning from others in the community can help navigate this evolving field and contribute to its development.
What Drives a Product Manager: The Exciting Facets of a PM’s Career
Effective management in product management involves three key aspects. First, tailor your approach to the needs of each individual on your team, recognizing that there is no one-size-fits-all solution. Second, invest in the long-term career growth of your team members, extending beyond the scope of your organization, by providing mentorship and opportunities for personal and professional development.
The third aspect involves being able to oversee the work of your team without micromanaging, while still being prepared to jump in and help when necessary. Balancing trust and autonomy with support is essential for successful management.
It’s an exciting time for all the PMs because we are focusing on doing good and building impactful products and services that can make people’s lives better.
In terms of current excitement in the field, AI and machine learning are opening many doors in product management. There’s a rewarding shift in focus in both healthcare and fintech industries. In fintech, increased emphasis on financial literacy and access to banking products for the unbanked population is driving positive change. Meanwhile, healthcare is moving towards value-based care, focusing on preventative measures and overall population health, which reduces costs and the burden on the healthcare system. This is an exciting time for product managers as they work on building impactful products and services that improve people’s lives.
As product managers continue to navigate this rapidly evolving field, learning from industry experts like Donna and sharing experiences within the community will be invaluable in driving growth and creating impactful products that make a difference in people’s lives. Key takeaways from our conversation include:
Defining success criteria for product stages: It’s crucial to consider both user and business perspectives when determining the success of a product.
Focusing on core competencies in optimization: Prioritize optimizing a product’s primary functions before expanding its scope or adding new features.
Conducting user research and embracing adaptability: Engage in user research, usability testing, and iterate on your product based on data and feedback, and remain open to change when necessary.
Effective management and exciting developments in the field: Tailor your approach to individual team members, invest in their long-term career growth, and maintain a balance between autonomy and support. Embrace the exciting opportunities in AI, machine learning, and the shifting focus of various industries.
The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.
Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?
Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.
Listen to our podcast to get some useful tips on your next startup.
In our CareMinds series, we’re all about showcasing the many paths to success in health tech product development. Today, we have the pleasure of sharing Laura Furman’s unique story. Laura, currently a senior product manager at Oura, kicked off her leadership journey with Students Agencies.
Laura opens up about her everyday work. She emphasizes the importance of AI and machine learning tools in her role, particularly during the product discovery phase, contributing significantly to the product’s development.
We hope you find Laura’s story as captivating as we did. Happy reading!
Is Product Development a Bold Claim or a Logical Step?
“The student agency’s experience is something that keeps coming back and keeps coming up as something that was really unique and an interesting foundation.”
Laura Furman – Senior Product Manager at Oura
Conventional wisdom may suggest that early experiences become less relevant as a professional journey progresses. However, Laura finds that her involvement with Student Agencies continually resurfaces as an integral part of her career. Student Agencies is a non-profit educational institution that provided Laura with first-hand business management experience during her college years. It comprised several diverse, student-run businesses, offering services from real estate property management to tutoring, marketing, and even a full-service moving company.
In this unique setting, Laura served as the general manager of one business for a year before stepping up to become the corporation’s president. Although not directly related to product management, this entrepreneurial experience provided Laura with an invaluable perspective on running a business from top to bottom, including direct customer interaction and budgeting.
In her role as president of Student Agencies, she created the first CTO role, utilizing the skills of the engineering students to enhance business performance. Despite the annual turnover inherent to the student-run structure, Laura credits an experienced CEO’s guidance for the continuity of the businesses. This end-to-end entrepreneurship experience, she believes, is a great asset for anyone entering product management, as it provides a comprehensive understanding of business strategy.
Transitioning to Product Management: A Personal Account
“I think a lot of people when they’re transitioning have a hard time, sort of filling the gap between where they are now and the skills they need to have as a product manager.”
Laura Furman – Senior Product Manager at Oura
Laura embarked on her career journey with uncertainty, opting for the retail industry as her starting point. She joined Gap’s management rotational program, and was tasked with e-commerce merchandising. Her role entailed strategizing the customer experience on Gap’s website, from product discovery to checkout.
As she delved deeper into her role, Laura identified a problem within one of the categories she was managing. This challenge provided an opportunity for her to explore business analytics extensively. She carefully examined every SKU, tracked trends across the assortment, and used this data to analyze the state of the business.
The most significant shift in her career occurred when she led a design sprint to rectify the problem in her managed category. This experience lit up her path towards product management, leading her to investigate job descriptions and key skills required for a product manager role. With several skills already under her belt and a drive to fill the gaps in her resume through projects and side assignments, she was ready to transition into product management.
The Evolving Role of a Product Manager
“My belief is we will produce the best ideas if we collaborate, I don’t think the PM should be coming up with all the solutions themselves, the solution should arise out of collaboration with the team”
Laura Furman – Senior Product Manager at Oura
The core of a product manager’s role is being the voice of the customer. It’s about understanding their needs, not just through face-to-face discussions but also through data analysis. As you step into it, remember that it’s not only about the customer’s desires, but also about striking the right balance with the business’s expectations.
When you craft your strategy, your ability to bring people onboard will be invaluable. Drawing from Laura’s experiences and skills in debate and negotiation, you’ll find that seeing multiple perspectives and effectively persuading others to join your journey can be a game changer. Additionally, remember that growing to be a product manager involves constant learning and iteration. You’d have to negate lengthy product road maps and promote a culture of continual testing and analysis.
Tools and Resources for Aspiring Product Managers
Taking the reins on your professional growth can be an empowering experience. One effective strategy to foster continuous learning is to dedicate each quarter to a specific focus area. This could start with understanding data analytics, mastering SQL, and becoming adept with tools such as Google BigQuery and Looker. This disciplined approach provides an opportunity to delve deeper into each field, enhancing your overall skillset.
Secondly, the value of mentorship in your journey cannot be overstated. A supportive and knowledgeable mentor can accelerate your growth, guide you through uncharted territory, and provide you with essential industry insights. Building connections within your organization is a beneficial way to learn from others and gain diverse perspectives.
Lastly, don’t let the fear of appearing unknowledgeable hold you back from asking questions. It’s a common misconception that asking basic questions exposes a lack of knowledge. In reality, it often leads to constructive conversations and enhances understanding. It’s essential to comprehend the bigger picture, particularly in understanding the system architecture. Spending time with engineers to grasp how different components of the system interconnect can provide invaluable insights. This broader understanding will be key in interpreting project estimates accurately.
How AI and Machine Learning Are Impacting Product Development
“The AI and machine learning tools that you use in your day to day…it provides an uncanny ability to tap into a problem, a domain that you don’t necessarily know a lot about. And it could quickly kind of guide you towards some potential solutions that could be applied to a certain identified problem if you don’t have a deep enough context to it.”
Laura Furman – Senior Product Manager at Oura
Mercari, a popular Japanese peer-to-peer marketplace akin to eBay, has an intriguing blend of challenges and experiences. The platform accommodates a broad array of categories, including clothing, technology, home goods, and handmade crafts. With this vast spectrum of products, one interesting challenge is managing User Generated Content (UGC). The diversity in UGC listings and searches can lead to discrepancies and inconsistencies due to differences in syntax, which in turn could reduce the visibility of items in search results, thereby affecting sales.
One notable project tackled at Mercari was enhancing the search and listing experience based on the brand and category of an item. The goal was to pre-populate custom attribute fields specific to the item type being listed. For instance, if a user is listing an iPhone, they could specify the model and size, allowing potential buyers to filter down their searches effectively. This approach was particularly useful in more subjective categories like clothing, where the search could be as specific as ‘straight leg jeans.’
To add another layer of sophistication, machine learning was brought into the mix. This technology helped predict necessary custom attribute fields based on the brand and category. It also fed these attributes into Mercari’s search taxonomy to optimize search results. Towards the end, the project began to utilize computer vision to guess the category and subcategory of clothing based on the photo. While this presented new challenges due to the variety of user-submitted photos, it also offered a fascinating direction for further enhancing user experience on the platform.
Sure AI and ML Complement Product Development, But How Can Managers Put Them to Effective Use?
First, from a day-to-day operational perspective, AI could serve as a sounding board, albeit it may not replace the nuanced understanding and context that comes from someone deeply familiar with the product. The idea here is that AI tools might not fully grasp the complexity of the product and its dynamics like a human member of the team who is immersed in the project.
The second application is more exciting: using AI tools to create prototypes. This could be especially beneficial for non-technical PMs who don’t have coding skills. They could potentially leverage AI to write code and thus develop prototypes, enhancing their ability to demonstrate their ideas beyond mere words. While there’s skepticism that AI could generate a feature-ready piece of code given the uniqueness and standards of any given codebase, using AI to create initial prototypes could be an innovative approach that empowers PMs to delve more into the technical side.
It is also believed that AI could streamline the process of creating a prototype, saving valuable time. This makes AI an attractive tool in the product management space, not just for its potential to enhance the overall workflow, but also to empower product managers with new capabilities.
From Novelty to Necessity: Does a Fresh Perspective Matter When Companies Hire?
Laura’s journey to AA three years prior was primarily driven by a long-standing personal passion for health and wellness. After reading “Why We Sleep” by Matt Walker, she developed an interest in the importance of sleep for mental performance and overall wellbeing. Tracking sleep with an “Oura” ring and studying the data became an obsession, eventually leading her to a position within the company. Her shared vision with the company’s CEO, who viewed sleep as the foundational pillar of health much like personal training, created a strong connection.
Her career transition strategy involved balancing industry experience and role skills as two vital variables. Initially, she drew upon her retail industry experience while developing necessary product skills. In the next move to AA, she utilized these newly acquired skills despite not having prior industry experience. Laura believed that possessing either industry experience or role-specific skills could facilitate a successful transition.
Laura’s perspective emphasizes seeing personal strengths as valuable contributions to her role and not being discouraged by perceived shortcomings. This outlook, particularly essential in product management, is about leveraging unique experiences and skills to meet new challenges in different industries. She also understands the importance of this mindset in successfully navigating work within a remote team, such as the Finnish-based company AA.
Understanding and Improving Predictable Delivery
Working across different time zones and geographies is challenging. While Laura has experience in this from her time at Merri, dealing with a 10-hour time difference at her current company has brought new challenges. She has realized the importance of well-prepared and efficient meetings, especially given that her early morning is the end of the workday for her colleagues in Finland. A critical success factor in such settings is robust asynchronous communication, making sure everyone is fully prepared and discussions are fruitful. In addition, they have implemented a system of reviewing and improving their workflow at the end of every cycle, accepting the reality of time differences but striving to make it better with each iteration.
One key learning Laura shared is the downside of over-relying on Slack for communication. It can create confusion, lead to critical information being missed, and ultimately decrease overall happiness within the team. Instead, they have focused on making communication more structured and traceable, using tools like Sigma, Jira, and Confluence to comment directly on project documents, ensuring a clear source of truth. If there is an excessive use of Slack, it’s often a sign that the project is experiencing chaos and needs attention.
When it comes to product improvement, Laura’s approach is guided by lessons from her mentor from Google. Objectives and Key Results (OKRs) should be ambitious, and achieving 70% of an OKR is a commendable feat. Key Performance Indicators (KPIs) are used more at the feature level, measuring specific outcomes. She emphasizes the importance of treating the development of a feature as a hypothesis – if they do X, they should see Y outcome in the data. This approach then allows them to review and learn from the outcome, guiding the development of future features.
Here are the most important points from our conversation with Laura Furman:
Early experiences matter
Laura’s involvement with Student Agencies during her college years, a non-profit educational institution that provided first-hand business management experience, played a crucial role in shaping her professional journey.
Transitioning is possible with the right skills and drive
Despite starting in a seemingly unrelated field (retail industry), Laura managed to transition to product management by building on the skills she had and bridging gaps through projects and side assignments.
Adaptability and continual learning are key in product management
The product manager’s role is not stagnant; it evolves with customer needs and business expectations. It also involves continuous learning, testing, and analyzing to stay ahead.
AI and ML are powerful tools in product development
These technologies not only assist in operational efficiency but also empower product managers, especially those with limited technical skills, to visualize and prototype their ideas.
Personal strengths and unique perspectives are valuable asset
Even if you lack industry experience, personal strengths, skills, and a fresh perspective can be instrumental in succeeding in new roles and different industries.
The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.
Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?
Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.
Listen to our podcast to get some useful tips on your next startup.
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.
How to make your IT project secured?
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.
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.
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
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!
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.
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.
What solutions can we offer?
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.
AI is solving routine paper, managerial, administrative processes that take time for doctors and nurses.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
Free access to datasets and data analyzing tools at cloud scale has a significant impact on the research process, especially in the global response for combating COVID-19.
As a Google Cloud Platform Partner, we want to share information with researchers, data scientists, and analysts about available hosted repositories of public datasets for tracking the COVID-19 outbreak.
Free datasets provide access to essential information eliminating the need to search for onboard large data files. You can access the datasets, along with a description of the data and sample queries to advance research from within the Google Cloud Console. All the data GCP includes in the program is public and freely available, while the program will remain in effect until September 15, 2020.
You can also use these datasets and BigQuery ML for training your machine learning model inside BigQuery at no additional cost.
Currently, Google Cloud Platform datasets include the following databases:
With all these databases and BigQuery ML, you can develop a data-driven model for the spread of this infectious disease, better understand, study, and analyze the impact of COVID-19. Together with the Google Cloud team, we believe that the COVID-19 Public Dataset Program will enable better and faster research to combat the spread of this disease.
Digital biology, also known as biotechnology and digital biotech, gives bioengineers, medication producers, agricultural companies, and industrial businesses excellent opportunities. Biotechnicians can turn biomaterials – living systems and organisms – into a digital data format, organize it, discover hidden patterns, and store it in databases.
Why does it matter?
Such an approach streamlines the research, development, and test stages of biology projects that previously took bio technicians months or years. Moreover, medical specialists apply digital biology to diagnose health conditions, such as cancer and sepsis, within several hours and suggest the most appropriate treatment based on patient samples.
Digital biology took a leap in development by applying Artificial intelligence and machine learning algorithms that automate biological data analysis and research. Thus, bioengineers generate more data in shorter terms, compared with the analog study methods they used previously.
In this article, you’ll find the current state of digital biology and the fields it serves. You’ll also read about biotechnology areas that benefit the most from other intelligent technologies, such as AI, machine learning, and cloud computing.
The current state of the Digital Biology market
Digital biology is a cross-disciplinary field that combines both biological and technological components. It includes exploring and analyzing living organisms with new intelligent tools.
Recognizing the considerable potential of biotechnology, governmental organizations, such as the National Institute of Biomedical Imaging & Bioengineering in developed nations and the National Center for Biotechnology Information, increased their investments into the research and development activities in biotechnology fields.
The market research from Global Market Insights (GMI), a global market research and management consulting company, says an increased interest from governmental organizations is expected to make biotechnology the largest and the fastest-growing market, projected to reach $729 billion by 2025, compared with $417 billion in 2018.
The research also includes a forecast of revenue increase for the following technology segments:
Tissue engineering and regeneration
In particular, the fermentation segment is the most prominent sub-niche of biotechnology that received an 11% revenue share of the whole biotechnology market in 2018.
The report predicts substantial progress for fermentation technology during the next few years. Fermentation is a process that changes organic substrates on the chemical level by enzyme action and micro-organisms.
Such growth of fermentation technology is explained by excessive use in the food and beverage industry. The food and beverage industry’s key business players will increase investments in biotechnology to improve the research and development activities to produce more fermented products.
The expected growth of biotechnology opens new opportunities for biotech startups, well-established companies, and research institutions. Another reason for biotechnology’s rise is various applications in medication, agriculture, and other industries.
Biotechnology Application Outlook
Digital biology, or biotechnology, includes several categories of applications. Biological technicians and other scientists apply digital biotechnology for solving scientific problems with living organisms across various industries – from healthcare and agricultural to industrial processing and bioinformatics.
Let’s see how each category benefits from artificial intelligence and machine learning.
In medical biotechnology, scientists receive information from living cells to get a clearer picture of human health, thus producing the most appropriate drugs and antibiotics.
Bio technicians dig into the smallest details to achieve these goals – study DNA and manipulate cells to predict beneficial and vital characteristics.
The most useful tech solutions used in medical biotechnology are Artificial Intelligence and machine learning that enable scientists to improve their drug discovery process by reaching small molecules and their target structures they need to treat.
In this way, doctors provide more timely and accurate treatment for better outcomes.
Moreover, artificial intelligence and machine learning are used in electronic health record (EHR) systems and clinical decision support systems to help doctors suggest a patient’s personalized medical treatment and accurate medication management.
Food and agriculture
Agricultural biologists apply biotechnology to increase crop yields, genetically modified plants, and identity infected crops before the harvest. For these purposes, scientists use DNA sequencing devices and databases with DNA samples of already sequenced genes. Once new DNA samples are sequenced, scientists can change their structure, learn more about the plant origins and potential issues typical for one or other plant.
[Increasing application of cell line engineering will drive the overall market expansion]
Food and agriculture biotechnology companies apply AI-algorithms to harvest crops, watch crop health, and find AI-powered tools more effective than humans.
Such an application requires food and agriculture businesses to integrate autonomous robots or drones, computer vision algorithms, and deep learning technologies. While drones and robots carry cameras, algorithms analyze crop pictures they receive, compare data captured with crop images in their database, and define whether crops and soil are healthy or not.
Industrial biotechnology includes research on biopolymer substitutes, inventions of vehicle parts, alternative fuels, new chemicals, and the process of production. In this area, intelligent technologies and the Internet of Things (IoT) devices help industrial producers analyze their machinery to predict outages, optimize equipment, and even reduce human worker numbers with automated warehouse management.
One example is Ocado Technology, an online grocery retailer that automated its warehouse with 3500 robots to process 220,000 online orders a week for grocery delivery.
To learn more about AI and machine learning applications in industrial processing and supply chain, check out our previous article about top AI applications in supply chain optimization.
Bioinformatics is a subdiscipline of digital biology that combines biology and computer science to acquire, store, analyze, and disseminate biological data, such as DNA and amino acid sequences. Scientists understand biological information using mathematics, data science, and different digital biology tools by organizing it in large biological pools.
Bioinformatics also receive benefits from AI and machine learning. Artificial intelligence and machine learning help biologists sequence DNA from the massive data crunch, classify proteins, protein catalytic roles, and their biological functions. Leveraging intelligent technologies, scientists can automate gene expressions and gene annotation and identify the location of genes required for computer-aided medication design.
In digital biology, biotechnologists base their research on digital data, generated from life samples or DNA sequencing devices and stored in a thousand databases, both private and public.
So we can conclude that the growing biotechnology industry will heavily rely on AI-algorithms, machine learning, and data analytics. But the development of biotechnology across all segments depends on biotechnology researchers’ ability to master their skills for the useful contribution of their findings and researcher results. Not only AI makes biotech engineers more efficient, but also for a bunch of other reasons.
Let’s check them out.
Top 3 advantages of using AI in the biotechnology industry
But why do scientists prefer digital biology to the old-but-gold analog approach?
Development and research projects often require scientists to deal with numerous amounts of data and large sample sizes, such as genome sequencing. In such cases, biological test digitalization allows researchers to produce more data than analog study methods. And by applying digital biology, scientists can receive real-time insights into biological functions which have taken them days and weeks when using an analog approach.
The adoption of AI and machine learning by biology specialists make the digital biology approach even more useful. And here is how:
Artificial intelligence and machine learning algorithms help bio technicians make more precise predictions than standard approaches used for decades. Successfully applied in supply chain and logistics, predictive analytics drastically reduce the time biotech companies spend to launch new products to market.
To make data-based decisions and forecast outcomes, data scientists train algorithm models with historical databases. Then, such algorithms are effectively used for pattern recognition, despite the data type.
As Nature online resource highlighted, intelligent algorithms’ ability to analyze large amounts of data in datasets helps drug-producing companies make new pharmaceuticals quicker and more effective. Soon, medication specialists will provide more personalized treatments, based on the disease’s cause, hidden deeply in biological structures. In this way, pharmaceutical companies can reduce the medicine development process from the $2.6 billion price tag and decrease the 90% failure rate of new medication created.
In her article, Melanie Matheu, Ph.D. and founder at Prellis Biologics, Inc. the human tissue engineering company predicts the new generation of therapeutics entering drug pipelines empowered by AI screening for selecting targets will reduce clinical trial failure rates for small molecules by 86%.
Clinical trials used to be manual and a very time-consuming process – they included inviting participants to the clinic during the in-person visit, recording their symptoms, prescribing them treatments, and analyzing side effects. Moreover, to get the right sample size, medication companies heavily invested in marketing resources for recurring right patients and treating rare conditions.
Now, intelligent algorithms and cloud technologies digitized clinical trials and enabled biotech organizations to test medication on more patients within less time.
One example is Invitae, a medical genetic company. In November 2019, the company launched a trial in collaboration with Apple Watch to bring together biometric data from wearables and genetic tests and determine genes that cause cardiovascular disease. In this way, the company made the trial available to many people and excluded Apple Watch users who didn’t meet the trial criteria.
Biotech companies make clinical trials even more effective by leveraging machine learning algorithms that analyze data from current trials and use it for forecasting treatment effectiveness in the future, down to a molecular level. ML also helps scientists revise information from previous tests to find gaps and new applications for existing medications.
Modern devices, cloud databases, data analytics pipelines, and machine learning algorithms reduced the cost of genome sequencing from $2.7 billion for the Human Genome Project to less than $300 by now. It is expected to cost even less – $100 in the future. Bioengineers receive more extensive screening of trial participants and targeting of interventions. They also see the future in personalized treatment plans and targeted therapies that provide therapies at genetic and molecular levels of patient genes.
The main area for targeted therapy is cancer treatment – the treatment of blood cancer such as leukemia, where a treatment called CAR T-cell therapy, according to the National Cancer Institute, the immune system will “attack tumors,” so we’ll soon witness more cancer survivors.
Biotech organizations also use cloud computing to host and run computations and no longer need to buy expensive computer hardware for their research. This fact is a substantial benefit for early-stage startups with limited funding to enter the market with their research and medications. Cloud computing is also handy for established medical corporations, allocating resources for new projects cheaper and more manageable.
What is the future of AI and machine learning in the biotechnology industry?
Biotechnology is an innovative industry that effectively solves scientific problems with living organisms. But new issues continuously arise and require biotechnologists applying modern methods to be solved.
Thus, to remain relevant, biotech specialists must make room for improvements. Fortunately, there are many solutions they can apply – AI, data analytics, deep learning, and others we’ve already listed in this article.
Thus, AI, machine learning, and robotics play critical roles in pushing the boundaries of possibilities in medical, industrial, or agricultural biotechnologies, and will remain relevant for subsequent decades.
The APP Solutions has experience developing and integrating AI functionality into biotech projects. You can learn more about our expertise in creating a real-time DNA sequence analysis application during our partnership with the Google Cloud Platform and the Queensland University of Technology. Don’t hesitate to contact us if you need experts to advise and develop intelligent software for your biotech project.
Google Cloud Platform provides various services for data analysis and Big Data applications. All those services are integrable with other Google Cloud products, and all of them have their pros and cons.
This article will review what services Google Cloud Platform can offer for data and Big Data applications and what those services do. We’ll also check out what benefits and limitations they have, the pricing strategy of each service, and their alternatives.
Cloud PubSub is a message queue broker that allows applications to exchange messages reliably, quickly, and asynchronously. Based on the publish-subscription pattern.
[Visualization of PubSub workflow]
The diagram above describes the basic flow of the PubSub. First, publisher applications publish messages to a PubSub topic. Then the topic sends messages to PubSub subscriptions; the subscriptions store messages; subscriber applications read messages from the subscriptions.
A highly reliable communication layer
10 MB is the maximum size for one message
10 MB is the maximum size for one request, which means if we need to send ten messages per request, the average total length for each notification will be 1 MB.
The maximum attribute value size is 1 MB
You pay for transferred data per GB.
Analogs & alternatives
Azure Service Bus
Other Open Source Message Brokers
Google Cloud IoT Core
[The architecture of Cloud IoT Core]
Cloud IoT Core is an IoT devices registry. This service allows devices to connect to the Google Cloud Platform, receive messages from other devices, and send messages to those devices. To receive messages from devices, IoT Core uses Google PubSub.
MQTT and HTTPS transfer protocols
Secure device connection and management
You pay for the data volume that you transfer across this service.
Analogs & alternatives
AWS IoT Core
Cloud Dataproc is a faster, easier, and more cost-effective way to run Apache Spark and Apache Hadoop in Google Cloud. Cloud Dataproc is a cloud-native solution covering all operations related to deploying and managing Spark or Hadoop clusters.
In simple terms, with Dataproc, you can create a cluster of instances on Google Cloud Platform, dynamically change the size of the cluster, configure it, and run MapReduce jobs.
Fully managed service means you need just the right code, no operation work
Dynamically resize the cluster
No choice of selecting a specific version of the used framework
You cannot pause/stop Data Proc Cluster to save money. Only delete the cluster. It’s possible to do via Cloud Composer
You cannot choose a cluster manager, only YARN
You pay for each used instance with some extra payment—Google Cloud Platform bills for each minute when the cluster works.
Analogs & alternatives
Set-up cluster on virtual machines
[The place of Cloud Dataflow in a Big Data application on Google Cloud Platform]
Cloud Dataflow is a managed service for developing and executing a wide range of data processing patterns, including ETL, batch, streaming processing, etc. In addition, Dataflow is used for building data pipelines. This service is based on Apache Beam and supports Python and Java jobs.
Combines batch and streaming with a single API
A fully managed service, no operation work
Dynamic work rebalancing
Based on a single solution, therefore, inherits all limitations of Apache Beam
The maximum size for a single element value in Streaming Engine is 100 Mb
Cloud Dataflow jobs are billed per second, based on the actual use of Cloud Dataflow.
Analogs & alternatives
Set-up cluster on virtual machines and run Apache Beam via in-built runner
As far as I know, other cloud providers don’t have analogs.
Google Cloud Dataprep
[The interface of Dataprep]
Dataprep is a tool for visualizing, exploring, and preparing data you work with. You can build pipelines to ETL your data for different storage. And do it on a simple and intelligible web interface.
For example, you can use Dataprep to build the ETL pipeline to extract raw data from GCS, clean up this data, transform it to the needed view, and load it into BigQuery. Also, you can schedule a daily/weekly/etc job that will run this pipeline for new raw data.
Simplify building of ETL pipelines
Provide a clear and helpful web interface
Automate a lot of manual jobs for data engineers
To perform ETL jobs, Dataprep uses Google Dataflow
Works only with BigQuery and GCS
For data storing, you pay for data storage. For executing ETL jobs, you pay for Google Dataflow.
Cloud Composer is a workflow orchestration service to manage data processing. Cloud Composer is a cloud interface for Apache Airflow. Composer automates the ETL jobs. One example is to create a Dataproc cluster, perform transformations on extracted data (via a Dataproc PySpark job), upload the results to BigQuery, and then shut down the Dataproc collection.
Fills the gaps of other Google Cloud Platform solutions, like Dataproc
Inherits all advantages of Apache Airflow
Provides the Airflow web UI on a public IP address
Inherits all rules of Apache Airflow
You pay only for resources on which Composer is deployed. But the Composer will be deployed to 3 instances.
Analogs & alternatives
Custom deployed Apache Airflow
Other orchestration open source solution
[Example of integration BigQuery into a data processing solution with different front-end integrations]
BigQuery is a data warehouse. BigQuery allows us to store and query massive datasets of up to hundreds of Petabytes. BigQuery is very familiar to relational databases by their structure. It has a table structure, uses SQL, supports batch and streaming writing into the database, and is integrated with all Google Cloud Platform services, including Dataflow, Apache Spark, Apache Hadoop, etc. It’s best for use in interactive queuing and offline analytics.
Huge capacity, up to hundreds of Petabytes
Batch and streaming writing
Support complex queries
Shared datasets — you can share datasets between different projects
All popular data processing tools have interfaces to BigQuery
It doesn’t support transactions, but those who need transitions in the OLAP solution
The maximum size of the row is 10Mb
You pay separately for stored information(for each Gb) and executed queries.
You can choose one of two payment models concerning executed queries, either paying for each processed Terabyte or a stable monthly cost depending on your preferences.
Analogs & alternatives
Azure Cosmos DB
Google Cloud BigTable is Google’s NoSQL Big Data database service. The same database powers many core Google services, including Search, Analytics, Maps, and Gmail. Bigtable is designed to handle massive workloads at consistent low latency and high throughput, so it’s an excellent choice for operational and analytical applications, including IoT, user analytics, and financial data analysis.
Cloud Bigtable is based on Apache HBase. This database has an enormous capacity and is suggested for use more than Terabyte data. One example, BigTable is the best for time-series data and IoT data.
Has good performance on 1Tb or more data
Cluster resizing without downtime
Support API of Apache HBase
Has bad performance on less than 300 Gb data
It doesn’t suit real-time
It doesn’t support ACID operations
The maximum size of a single value is 100 Mb
The maximum size of all values in a row is 256 Mb
The maximum size of the hard disk is 8 Tb per node
A minimum of three nodes in the cluster
BigTable is very expensive. You pay for nodes (minimum $0.65 per hour per node) and storage capacity(minimum 26$ per Terabyte per month)
Analogs & alternatives
Custom deployed Apache HBase
GCS is blob storage for files. You can store any amount of any size files there.
Good API for all popular programming languages and operating systems
Versions of files
Suitable for any size files
Suitable for any amount of files
GCS has a couple of pricing plans. In a standard plan, you pay for 1Gb of saved data.
There are a few more services that I should mention.
Google Cloud Compute Engine provides virtual machines with any performance capacity.
Google CloudSQL is a cloud-native solution to host MySQL and PostgreSQL databases. Has built-in vertical and horizontal scaling, firewall, encrypting, backups, and other benefits of using Cloud solutions. Has a terabyte capacity. Supports complex queries and transactions
Google Cloud Spanner is a fully managed, scalable, relational database service. Supports SQL queries, auto replication, transactions. It has a one-petabyte capacity and suits best for large-scale database applications which store more than a couple of terabytes of data.
Google StackDriver monitors Google services and infrastructure, and your application is hosted in a Google Cloud Platform.
Cloud Datalab is a way to visualize and explore your data. This service provides a cloud-native way to host Python Jupyter notebooks.
Google Cloud AutoML and Google AI Platform allow training and hosting of high-quality custom machine learning models with minimal effort.
Now you are familiar with the primary data services that Google Cloud Platform provides. This knowledge can help you to build a good data solution. But, of course, Clouds are not a silver bullet, and in case you use Clouds in the wrong way, it can significantly affect your monthly infrastructure billing.
Thus, carefully build your proposal’s architecture and choose the necessary services for your needs to reach your needed business goals. Explore all benefits and limitations for each particular case. Care about costs. And, of course, remember about the scalability, reliability, and maintainability of your solution.
Healthcare is one of those industries that embrace cutting-edge technologies and makes the most of them. The reason for this is simple – new technologies that help save people’s lives and increase the quality of life.
The adoption of machine learning and natural language processing algorithms throughout the healthcare industry has helped to streamline and vastly improve workflows of different medical procedures, and as a result, has made them more effective for their causes.
Some of the most prominent examples of such streamlining and improvements are patient support systems. Let’s explain why.
What’s wrong with patient support?
The word that best describes the state of patient support in the healthcare industry is “overwhelming”. Unlike other fields of the healthcare industry, where the root of the problem lies in the methodology, in the case of patient support it is the scope of the operation. In other words, too much demand and too little supply.
Just like regular customer support everywhere else, the primary issues are:
Workflow efficiency. Because of limited resources, there is a tendency towards bottlenecks in the support pipeline. This prolongs the processing of the request and subsequently stretches out the reply time for a single request. As a result, the request processing pipeline is severely undercut.
Workforce turnaround. Due to the high workload and punishing schedules, support operators often burn out and quit.
Availability of the service. It is hard to maintain a fully-fledged 24/7 support service, beyond simple Q&A automation, with a limited workforce.
Bringing onboard new employees takes time.
Operational costs. In addition to employee salaries, there are infrastructural maintenance costs.
In one way or another, these issues are solved with process automation and adoption of Chatbot and Natural Language Processing.
NLP Chatbot creates a win-win situation, both for healthcare service providers, and patients.
Companies are able to optimize the workflow;
Chatbot reduces the workload of the human operators while making the service available 24/7.
Patients get a much more engaging and efficient service.
Conversational UI chatbots take over the majority of routine conversations, such as results notification and Q&A. Human operators are involved only in special cases;
Natural Language Processing provides a deeper dive into the intent and sentiment of the user’s requests;
This information gives ground for process automation that increases the speed of delivery up to 40%;
The implementation of the conversational interface chatbot lowers the operational costs up to 30%.
Our company was approached to develop such a solution and implement it into the existing system infrastructure. Let’s look at how The App Solutions developed a Chatbot solution for Healthcare patient support.
How Chatbot can create a more efficient patient support system?
The client had a patient support system that handled a wide scope of patient requests such as:
Providing various notifications – like test results, examination registering, etc;
Solving emerging issues – for example, retrieving lost passwords or explaining how to use different features of the service;
Gathering user feedback on the support service itself, and other services of the company.
The entire operation was handled by trained human operators who worked under a strictly regulated set of guidelines.
And while the workflow was fine-tuned, it wasn’t sufficient enough for the scope of the operation. With over a million active users, the patient support system resources were stretched too thin.
In addition to this, there were concerns regarding the use of sensitive information and the possibility of compromising the integrity of the users’ accounts.
Because of this, it was decided to completely overhaul the patient support system with cutting edge technologies.
Our task on the project can be described as follows: to develop a reliable solution that would:
Streamline the workflow of the customer support operation;
Keep sensitive information safe.
The key requirements were to:
Implement a chatbot and enable 24/7 support;
Implement process automation for basic conversations and actions;
Increase the request resolution time;
Deploy the system on the cloud platform and make it more scalable for large scale data processing;
Keep the system fully compliant with the current privacy regulations.
Here’s how it went down:
Training a language model
Process automation and Chatbot interface require a pitch-perfect understanding of the request intent and subsequent trigger of the course of action. The former part is handled by the NLP language model.
We have used a combination of Word2Vec and Doc2Vec to train the model and optimize the generative algorithm.
[Example of Doc2Vec mechanism]
[Example of Word2Vec mechanism]
Due to the specifics of the healthcare topic, the use of the open-source datasets is somewhat limited. They can be used to provide a groundwork for the model, but further training and optimization requires more peculiar data taken directly from the system.
In order to train a language model on the best fitting dataset – we compiled it ourselves from patient support conversations. We used unsupervised machine learning algorithms to explore patient support data and then applied supervised machine learning algorithms to shape it into a training dataset.
The chatbot was the main system component. With a language model intact – our goal was to construct the interface around it.
The model was developed with Python NLTK and Chatterbot library. After that, it was migrated to the web interface with Flask API.
We implemented a couple of machine learning algorithms to determine the intent of the request and connected it with relevant actions. For example, if the patient asks about doctor working hours – the bot accesses the doctor’s calendar and provides the relevant information.
The main challenge at this stage was making the interface accessible. Since the level of technical literacy of the users may vary – we needed to make the whole thing as simple to use as possible.
In order to do this, we applied extensive A/B testing of the functional elements. This allowed us to streamline the interface design and also optimize the conversation design.
Implementing process automation with machine learning
After developing a working language model and constructing a conversational UI chatbot around it, our next step was to prepare the process automation routines that would be activated from the chatbot interface.
In order to do that, we broke the task into three categories:
Service support automation – the ones related to the services themselves (such as booking an examination or requesting test results).
Maintenance automation – related to system support and general information (for example, how to retrieve a lost password or to proceed with checkout)
Switch to human operator scenario – for complicated or emergency cases
We identified keywords and intentions for action triggers with TF-IDF.
In order to broaden the scope of the model, we combined them with a wide selection of phrase variations so that the routine would be activated through casually formulated input queries.
In order to secure consistent system performance, we deployed the entire project into the cloud platform.
In this way, the patient support chatbot can maintain a high turnaround of information in the system, and a large volume of different operations, without slowing down or experiencing technical issues.
Google Cloud Platform autoscaling features provided a solid backbone for every operation in the system and neutralized possible emergence of the scalability issues.
Implementing Data Security solution
Privacy and confidentiality are amongst the central concepts of healthcare services. In the case of patient support systems, this is one of the most important elements. Not only do you need to guarantee the security of data in general, but you also need to guarantee that the whole interaction between the service and the patient is absolutely confidential.
The whole data processing operation must be compliant with the Personal Information Protection and Electronic Documents Act (PIPEDA).
In order to maintain PIPEDA compliance, we implemented the following solutions:
Provided a detailed description of how user personal data is used on the service;
Expanded a consent agreement for data processing upon registration;
Limited retention of data from deleted accounts to 30 days after termination.
Implemented Transport Layer Security (TLS) with a 256-bit Advanced Encryption Standard for data transit.
Google Cloud Platform
NLTK for Python
Word2Vec / Doc2Vec
The project reinvigorated the company’s patient support.
The implementation of the chatbot interface cut operational costs in half.
The NLP component increased the efficiency and availability of the service. As a result, the response time period was decreased by a third.
The process automation allowed to streamline the service’s workflow and minimize the role of human operators in the handling of sensitive data.
On the other hand, this project was a huge accomplishment for our team. We developed a complex solution that managed to bring the whole patient support system to a new level with a much higher efficiency rate.
During the development of this project, we utilized more streamlined workflows that allowed us to make the whole turnaround much faster. Because of this, we managed to deploy an operating prototype of the system ahead of the planned date and dedicated more time to its testing and refinement.
Predictions suggest that 80% of businesses will use chatbots by 2020. If you still haven’t integrated a chatbot to your business operations, you may be falling behind the competition. However, you have an opportunity to develop one in 2020. But before hiring chatbot development companies you need to be aware of the most popular chatbot trends for 2020.
Below we have gathered industries that apply chatbots, benefits chatbots bring to business operations, and main trends to build a conversation interface for your business.
Chatbot Overview: adoption across different industries
Many industries currently apply chatbots however, the effectiveness of user interfaces varies from one industry to another. To find out whether a chatbot will suit your particular industry, check out the top industries profiting from chatbots:
Every person who is looking for a house or apartment to buy has unique requirements. Real estate chatbots help businesses to gather customers’ needs for more personalized recommendations and validate leads, which helps sales managers spend less time answering questions.
Apart from more personalized product recommendation chatbot usage, online retailers use chatbots to streamline the sales process. Now, chatbots help customers search for a product, place an order and pay for it, and even track the delivery of the order.
Travel agencies use chatbots to help travelers to find the best trip, book a hotel, and even buy tickets. Besides this, chatbots are handy for providing travelers with local insights, weather forecasts and booking tables, and restaurants.
Artificial Intelligence-powered chatbots perform as intelligent tutoring systems, providing a personalized learning environment for students. Chatbots analyze a student’s response and how well they learn new material. Moreover, an AI chatbot can teach students by sending them lecture material in the form of messages, like in a chat.
In this industry, chatbots can automate each stage of communicating with a candidate. Recruitment agency chatbots can perform as advisors, automate the search for candidates, evaluate their skill set, and give feedback on whether or not a candidate qualifies for a particular job.
Healthcare chatbots help patients to book appointments, refill prescriptions, and remind patients to take medications on time. Moreover, more advanced chatbots can monitor a patient’s health periodically, make diagnoses, and give advice on treatment plans.
Increase income by 40%. Research shows that customers who interact with brands via social media networks spend 20-40% more than average customers. In this way, to receive the same benefits, you can integrate a chatbot into the social media accounts of your company.
Increase lead generation. Thanks to their proactive nature, chatbots can start communication with your clients, taking them through the sales funnel. Besides this, chatbots can event capture customer’s details, thus, generate more leads.
Increase user retention rate. As you may know, some chatbots are powered by Artificial Intelligence and machine learning. Thus, they can learn from each interaction and remember a client’s preferences. Since customers receive more personalized product recommendations, they become loyal to your brand.
Top 5 chatbot predictions for 2021
Now, let’s find out what the future holds for the chatbot industry.
1. Voice recognition chatbot technology
Voice recognition chatbots will become more widespread in 2020. Why? Because this year, Google and Amazon, recognized tech industry giants, continue driving the “smart speakers” market. For example, Amazon alone has sold 100 million devices with built-in voice assistant Alexa. Moreover, 110 million Americans use voice assistants at least once a month. This market trend shows us that voice-based chatbots, driven by tech industry leaders and voice-powered chatbot platforms, such as PullString, will become even more popular in 2020.
2. Smarter Bots
Rule-based chatbots no longer satisfy the needs of modern business, especially in terms of personal recommendations and customer engagement. Thus, it is expected that most companies that want to automate processes, will choose AI-based chatbots over scripted ones. As well as this, the high adoption of AI-based chatbots is also expected in mass media and live news. In this way, readers will no longer search for relevant news but instead receive personalized news recommendations.
[AI chatbot working logic]
3. Banking and insurance chatbots
Chatbots for the banking sphere allow delivering more personalized customer service. According to statistics, 43% of online banking users want to solve their issues via a chatbot.
A great example is Erica, a Bank of America chatbot that handles customer queries, anticipates customer needs by applying predictive analysis, and guides clients through complicated banking procedures.
[Erica, a Bank of America chatbot ]
As for the insurance sector, businesses will continue to adopt AI chatbots since they have proven their effectiveness. Chatbots help insurance companies to educate clients in various fields, including inspections, submissions, documentation claim adjustments, and update them on the status of their claims.
4. Data analysis
Chatbots are becoming, not only a new form of communication but also a sales channel. Moreover, AI chatbots are able to, not only provide users with more personalized and relevant results but also help in data mining and analytic activities. Thus, by analyzing customer data received from interaction with clients, businesses can get even more valuable insights.
5. Chatbot call centers
Chatbots are no longer only conversation interfaces. In the year 2020, chatbots are expected to be used for automated call centers. Call centers will use this technology to receive information about an issue, as well as personal details. Thus, based on the information received, chatbots can switch a customer to the most qualified human agent. In this way, chatbots reduce the waiting time and improve the quality of customer care.
The future of chatbots
[US chatbot market prediction]
In 2021, the chatbot market is expected to grow as over 80% of businesses will adopt chatbots. AI chatbots help businesses across different industries to automate sales, marketing, and customer care.
With the high adoption of voice assistants produced by Amazon and Google, in 2020, there will be even more which are voice-based. Furthermore, chatbots that participate in the sales funnel will become the new source of valuable customer information for online businesses. Next year, it is also expected that chatbots for banking and insurance companies will become even more popular.
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:
magnetic resonance imaging (MRI);
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:
Image enhancement can improve the quality of an image.
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.
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.
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.
Ready to apply data science in your healthcare organization?