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How Machine Learning Is Changing Medicine And Healthcare?

Aug 275 min read

Robert Krajewski

Co-founder and CEO of Ideamotive. Entrepreneur, mentor and startup advisor.

Innovations in technology have always influenced the development of medicine. It's true for artificial intelligence and machine learning as well. Although we still haven't achieved the level of self-aware computers able to take over control over humanity, we already can see how computers enhance our ability to solve problems and apply the treatment.

 

Machine learning in healthcare is now used for better processing of large amounts of data. It's become cost-effective and more affordable since the development of computing power occurred.

 

The application of machine learning is widely appreciated since evidence-based medicine is now a gold standard. Moreover, it gives us access to technology-enabled healthcare improving treatments, the efficiency of R&D, and the creation of new tools.

 

State of the AI & ML in healthcare in 2020

Many organizations work on the development and application of AI and ML in healthcare. They employ both medical experts, big data analysts, and hire AI developers. Think about any technological corporation, and it probably works on AI/ML medicine projects. IBM, Google, Microsoft. Together with start-ups and universities around the world, they work on the improvement of our well-being. It's a wide realm and there are virtually dozens of possible solutions powered by AI. Let's discuss the most popular application of AI in Medtech: handling administrative tasks with Natural Language Processing. 

 

How does it work? The computer either listens to you or reads your handwriting and then transforms it into medical information. It can also conclude your utterances. Machine learning makes it easier to create and maintain smart health records as well.

 

Accelerating medical research with AI and ML can be achieved through faster analysis and the design of medicaments, as opposed to the exploration and discovery. Moreover, tools for risk identification are based on big data collected from cohorts of patients. AI tools can forecast risk factors and indicate ways to prevent it.

 

Diagnosis and disease identification are both enhanced thanks to machine learning algorithms and access to big data sets gathered around the world. It also makes it possible to discover new drugs using the unsupervised learning techniques. Advanced algorithms enhance the capabilities of medical imaging diagnostics. AI-powered tools work with diagnosticians and suggest possible disease indicators.

 

Personalized medicine is one of the hot topics in Medtech. Dozens of mobile applications have promised to deliver such solutions. However, only with AI and ML, it will be possible.

 

Last but not least, artificial intelligence and machine learning are extremely useful for epidemic outbreak prediction. We will only have to learn how to use it properly.

Machine learning applications in Healthcare – 10 real-life examples

Medtech product development is growing rapidly. Here we gathered 10 examples of the most interesting and valuable products, services, and solutions that incorporate ML in healthcare.

KenSci

Risk prediction platform for healthcare institutions. KenSci integrates data from multiple sources (e.g. patient-generated). Data sets are then analyzed with pre-built machine learning models and modular solutions. Results of analyses enable physicians and payers to identify risk factors and vulnerable individuals and then apply optimal treatment or prevention activities. Earlier and more suited treatment leads to lower costs and better well-being.

IBM Micromedex

Powered by Watson, IBM Micromedex uses AI to enhance the ability to make data-driven decisions regarding medical treatment. It helps doctors decide on the treatment based on drug information, disease and condition management, or toxicology. They can easily get critical information straight from the conversational search assistant “Ask Watson™”. Using natural language processing, it boosts search capabilities and provides valid suggestions such as drug classes, dosing, and administration, IV compatibility, medication safety, mechanism of action, pharmacokinetics, and drug interactions. Micromedex mobile apps are available for iOS and Android mobile devices.

Skin Vision

SkinVision employs artificial intelligence to analyze the photos of spots on your skin to evaluate the risk of skin cancer. It aims to offer increased health benefits at lower costs. The system not only indicates the risk but also advises on what to do next.

 

All a user needs to do is take a photo of their skin. Clinically validated algorithm then asses the image. Next, the user is informed about the risk. Later the system will remind its users about regular skin-check to keep track of changes occurring over time. SkinVision claims their system has a sensitivity of 94% and specificity of 80%, both well above general practitioners or dermatologists.

Densitas for mammography     

The densitasai™ platform by Densitas Inc. provides breast density images analysis with results that are available on-demand for ma. It also offers a solution for retrospective evaluation and auditing of historical mammograms. To make it possible, Densitas delivers advanced analytics platform available for radiologists and health system administration. The artificial intelligence engine enables fully automated patient-specific image metrics.

 

Data in a web-based analytics platform can be accessed via interactive dashboards with filtering capabilities. The platform provides performance tracking, closed-loop traceability, worklist generation and prioritization, and automated report generation.

Quantx Qlarity Imaging

QuantX uses artificial intelligence and big data analysis to enhance radiologists' ability to interpret medical images. It is the first FDA-cleared computer-aided breast cancer diagnosis system used in radiology.

AI used in Qlarity Imaging analyzes breast MRI images. It can also generate a 3-D outline and provide volumetric and surface area analysis. The application of Qlarity Imaging results in a 39% reduction in missed breast cancer.

MD Insider

Societies call for increased transparency in healthcare. It's important for patients and paying institutions, but also for hospitals and doctors themselves.

This platform uses machine learning to evaluate doctors. It identifies key indicators of physicians performance. MD Insider delivers a complete data set and enables searching, matching, and scheduling to their clients. It also offers API services you can use to integrate with the system of your choice.

prognosFACTOR™

AI and machine learning algorithms from Prognos enhance the ability to diagnose and monitor patients as well as decide on their treatment. Starting with unstructured clinical data, the platform conforms to it, enriches using sophisticated AI and machine learning techniques, and finally delivers deep, data-driven clinical insights.

 

Specific diseases have various implications for patients, physicians, and payers. Deeper and more reliable insights help in making better, data-driven decisions. Prognos also ensures regulatory compliance and security of clinical data.

El Camino Hospital     

As in every hospital, patients of the El Camino Hospital are at risk of falling. To prevent that, the institution collaborated with Qventus company to create a system that will identify the most vulnerable patients. They put the EHR (electronic health record), bed alarms, and nurse call light data together and used prescriptive analytics on it. Patients at risk could now obtain personalized care aimed at proactive fall prevention. Hospital staff can now focus their efforts on the high-risk patients in real-time. What are the results? The 39 percent reduction in falls within the first 6 months.

 

Prealize

Prealize employs advanced machine learning to predict the qualities and time of future risks. The main idea is that if you know about the risk, you can avoid it or at least diminish its impact on your health. Healthcare providers can achieve better outcomes and healthcare experience for patients. Prealize suggests the most effective treatment and approach toward both individuals and populations. It also enhances the efficiency of health expenses by addressing those patients, who can benefit the most from the limited resources and which procedures will have the most significant impact.

AGH Agent Health

AGH is a Canadian company that offers AI as a Service (AIaaS). With AGH public institutions as well as private companies can get a tailor-made AI solution fitting their needs. AI solutions provided and developed by AGH will aggregate data sets to make the most of them enabling data-driven decision-making. Canadian company is focused on medical applications, but it offers solutions for other industries too.

How Machine learning helps to fight COVID-19?

Researchers around the world work to find out how ML can help in fighting the pandemic? We should aim at every aspect of the COVID-19 crisis to deal with the global impact on health and economy.

 

Scientists started with efforts to understand the virus and accelerate medical research on drugs and treatments. With this knowledge, we will know how long we should avoid socializing and stay alert. Furthermore, we will learn to prevent (or at least slow down) the spread of the virus. The analysis of data from contact tracing done with AI algorithms will be particularly beneficial here.

 

Machine learning in healthcare has already helped to discover the genome of SARS-CoV-2. Thanks to that it is possible to better diagnose patients, predict the evolution of the virus, and design drugs and vaccines. Moreover, combined with our knowledge of the human genome, it allows applying personalized treatment to the infected ones.

 

Chicago-based Medical Home Network has already shown how to use ML to identify high-risk individuals who are vulnerable at most. Later we can employ the algorithms to monitor recovering people and prevent them from re-infection.

 

Artificial intelligence is used to fight the infodemic as well. It is much faster and objective, and as a result more effective, in finding the fake news spread across social media and the Internet.

Where to find Machine Learning Experts?

The growing call for machine learning in healthcare encounters the problem of a relatively low supply of specialists in the field. In Ideamotive we know how hard it can be to find and recruit a Machine Learning expert. When you face this challenge, let us know. We've already gathered specialists experienced in various companies, backgrounds, and industries including Medtech services.

 

Tell us, who you need, and we will pass you to the machine learning specialists you need.

Robert Krajewski

Robert is a co-founder and CEO of Ideamotive. Entrepreneur, who with passion spreads digital revolution all around the internet. Mentor and advisor at startup accelerators. Loves to learn and discover new business models.

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