Healthcare AI & ML Investment Thesis

By Courtney Backstrom, Kamal Tagba, and Victor Khong — RBL1 Fellows and Members of the RBL1 Network

Applying AI to Transform Healthcare — How can AI and ML Enhance Care for Patients?

Market Background

Artificial intelligence (AI) can be defined as the development of novel algorithms to mimic human behavior and cognitive functions¹. Machine learning (ML), pattern recognition, recommenders, and neural sets are all technologies that can be characterized by AI². The capabilities of AI impact a wide array of industries. More specifically, AI is currently used to advance technology in the industries of manufacturing, education, business services, automotive, and healthcare³. In terms of AI’s applications to business, profit margins can dramatically benefit since AI can be used to optimize internal business operations by improving the efficiency and accuracy of a company’s decision-making process⁴. As for the automotive industry, an incremental approach has led to the development of collision avoidance, car-to-car communication, and self-parking features. However, fully-autonomous, self-driving cars are the future vision for innovators in this industry². The applications of AI reach much further than improving the efficiency of our everyday lives. AI plays an essential role in healthcare by performing automated medical-imaging diagnoses (i.e., X-ray radiography, computed tomography, magnetic resonance imaging) as well as in robotic-assisted surgeries where a surgeon’s hand movements are replicated by a robot on a patient⁵. Another reason behind the interest in health AI is the potential to identify medical errors such as missed diagnosis and misdiagnosis. In 2015, a study conducted by Martin Makary and Michael Daniel revealed that medical error is the third leading cause of death in the U.S, which is 10%⁶.

Of the many ways in which AI will transform our lives in the coming years, its impact may be more profound and far-reaching in healthcare than in any other field. Machine learning and healthcare are in many respects uniquely well-suited for one another. At its core, much of healthcare is pattern recognition.

In which specific areas of healthcare can we expect AI to have a significant impact? How can this technology help lessen health disparities between patient populations? It is helpful to break down the sprawling field of healthcare into three main categories: clinical (the delivery of care to patients such as imaging, digital health or patient engagement), administrative (provider operations, data infrastructure, medical documentation), and pharma (the research and development of new medical drugs). In each of these three areas, machine learning is already being applied in transformative ways. This will only accelerate in the years ahead.

Market Analysis

According to a 2017 Accenture report, the market size of health AI in 2014 was estimated to be $600 million and forecasted to grow to $6.6 billion in 2021, representing a 40% CAGR⁷. Another report by Reports and Data estimates that the healthcare AI market size will reach $61.59 billion in 2027, representing a 43.6% CAGR⁸. However, the COVID-19 pandemic has accelerated the growth in health AI so the actual numbers will likely be greater than the projections.

In this market, there are many players involved, focusing on different areas of health AI. Top players in the industry include Google Health/Deepmind, IBM Watson Health, and Oncora Medical⁹. However, the space is not dominated solely by large corporations. The number of startups in the space grew alongside the industry. In 2019, there were over 90 health AI startups that had one or more funding rounds of $10+ million since 2018. Among these startups, those focusing on imaging and diagnostics solutions took up the largest percentage followed by drug discovery¹⁰. Examples of startups focusing on imaging and diagnostics solutions are Caption Health, Overjet, and Radiobotics. Examples of startups focusing on drug discovery are Recursion Pharmaceuticals, Insitro, and Owkin. There are many other health AI startups that focus on other areas such as Unlearn.AI, Theator, and Olive.

Key Players and Companies

Service differentiation, increasing investments in R&D, and collaborations with key industry participants are the key strategies adopted by key competitors to gain a competitive edge in the industry. Moreover, rising investments in AI by startups are also propelling market growth. For instance, Analytics 4 life, a Toronto-based startup, raised around USD 29.0 million to develop a new medical imaging technology, using AI algorithms for cardiac diagnostics.

Some of the prominent players in the artificial intelligence in the healthcare market include:

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

Risk and Opportunity

The risks of incorporating artificial intelligence in medical devices include faulty or manipulated training data, attacks on AI such as adversarial attacks, violation of privacy, and lack of trust in technology. In spite of these technology-related risks, the applicable standards and regulatory frameworks do not include any specific requirements for the use of artificial intelligence in medical devices.

Another important question concerns the responsibility and accountability of artificial intelligence. Medical errors made by human doctors can generally be traced back to the individuals, who can be held accountable if necessary. However, if artificial intelligence makes a mistake the lines of responsibility become blurred. For medical devices, on the other hand, the question is straightforward. The legal manufacturer of the medical device incorporating artificial intelligence must ensure the safety and security of the medical device and assume liability for possible damage.

In clinical applications, artificial intelligence is predominantly used for diagnostic purposes. Analysis of medical images is the area where the development of AI models is most advanced. Artificial intelligence is successfully used in radiology, oncology, ophthalmology, dermatology, and other medical disciplines². The advantages of using artificial intelligence in medical applications include the speed of data analysis and the capability of identifying patterns invisible to the human eye.

Further AI Startups Analysis

The use of AI in healthcare has proven a myriad of advantages in several areas of the sector. Dozens of companies in the health sector using AI to improve health services, have emerged and are imposing the pace of innovation in the U.S.

CB Insights’ state of healthcare Q1 ’21 report features data-driven insights and provides a picture of an increasing interest of investors globally in the companies using AI to improve healthcare and the service¹¹.

Caption Health

The 70-strong sized company uses AI to interpret ultrasound exams¹². Founded in 2013, the point-of-care ultrasound technology helps healthcare staff with little to no experience of sonography capture high-quality ultrasound scans, helping to improve clinical decision-making. It ranks first in CB Insights in the report in terms of startups providing healthcare solutions with AI¹³. The California-based company has landed total funding of $60.7 million and $53 million in Series B. Its investors include Khosla Ventures, Georges Harik, Data Collective, Atlantic Bridge, Vince Monical, Edwards Lifesciences, 11.2 Capital, Greenbox Venture Partners, Minneapolis Heart Institute Ventures.

Recursion Pharmaceuticals

The company is the second most advanced healthcare startup using AI on the CB Insights’ reports¹⁴. Recursion is a biotech company that combines biology, chemistry, automation, AI, and data science to discover new drugs. In 2020 the company raised $239 million and formed a strategic partnership with Bayer to develop new treatments for chronic scar tissue and lung, kidney and heart conditions¹⁵. It boasts a market capitalization valued at $3.9 billion. In the Q1 of this year, the company founded in 2013 reported net revenue of $30,7 million and revenue of $2.6 million.

Unlearn.AI

A pioneer of the use of ‘digital twins’ in clinical studies — virtual replicas of devices or individuals that can be used to run simulations¹⁶. Unlear.AI has created virtual patients developed from real data to reduce the time it takes to carry out clinical trials. The startup is currently using machine learning to forecast the progression of Alzheimer’s Disease. Founded in 2017 in San Francisco. Unlearn.AI has so far raised $15 million and $3 million in the latest round, in November 2020. The company employs 29 people. Investors include EPIC Ventures, 8VC, Alumni Ventures Group, Data Collective DCVC, Mubadala Capital, Ventures US, Eisai.

Theator

The 2018-established company has developed a SaaS platform to address variability and disparity in surgical care¹⁷. Using AI and computer vision, it captures video data and transforms it into actionable insights, helping surgeons enhance their performance. Since its establishment in 2018, Theator raised $18.5 million with last funding in February in 2021 for an estimate of $15.5. Investors in the growth of the company include Blumberg Capital, StageOne Ventures, Eyal Gura, Anne Wojcicki, iAngels, Neil Hunt, NFX, KdT Ventures, Insight Partners. Theator employs 22 people and is located in CA.

Overjet

This Massachusetts-based company aims to help dentists determine what dental treatments a patient needs, aided by AI, computer vision, and data science¹⁸. A key differentiator is that Overjet charges insurance companies per claim, rather than per X-ray. The startup was incubated at the Harvard Innovation Lab. Overjet has total funding of $34.9 million. It raised $27 million in the last Series A round which happened in August this year. The startup currently employs 38 people. Its investors include Crosslink Capital, Liquid 2 Ventures, The E14 Fund, and Neoteny.

Olive

The Columbus-based startup provides artificial intelligence and robotic process automation solutions for healthcare organizations. The company’s enterprise AI is now in place at more than 900 hospitals in over 40 U.S. states, including more than 20 of the top 100 U.S¹⁹. health systems and around 976 employees. Valued at about $4 billion, the company has to date raised $902m since its inception in 2012. In the last round, in July this year, the company closed a $400-million funding round. Its investors include Silicon Valley Bank, Khosla Ventures, General Catalyst, Dragoneer Investment Group, Oak HC/FT, Drive Capital, Tiger Global, Vista Equity Partners, SVB Capital, Ascension Ventures, GV, Healthy Ventures, NCT Ventures, Moonshots Capital, Base10 Partners, Sequoia Capital Global Equities, Transformation Capital Partners. It made in April 2021 the acquisition of Empiric Health for an undisclosed value.

Insitro

Insitro is a machine-learning-driven drug discovery and development company. The startup has raised to date since inception $243 million in funding. The last round (B), took place in May 2020²⁰. Investors including Andreessen Horowitz, CPP Investments, Casdin Capital, HOF Capital, and WuXi AppTec’s Corporate Venture Fund wrote a cheque of $143 million meant to continue to build foundations of technology and automation, enabling data generation at a larger scale and further expanding the capabilities to generate predictive models of human disease as well as establish new, synergistic industry partnerships and build additional ML-enabled capabilities along the R&D value chain in order to accelerate drug discovery and development. Insitro employs 136 people and is located in South San Francisco.

Owkin

The 2016-founded NYC startup builds machine learning technologies and infrastructure to enable medical research. Algorithms support drug development by predicting treatment outcomes and disease evolution²¹. The company raised in March of 2020 an undisclosed amount of funding that brought its total funding to $60.9 million. Its investors include Nicole Junkermann, Bpifrance, Shana Fisher, Brent Hoberman, Bpifrance Large Venture, Otium Capital, GV, Jorg Mohaupt, Cathay Innovation, Plug and Play, NJF Capital, Jean-Paul Clozel, F-Prime Capital. The company employs 148 people.

Radiobotics

The startup, established in 2017, develops machine learning algorithms designed to automate the reading of x-rays of bone and joints and help in making medical decisions. algorithms automate routine tasks and alleviate reports, enabling doctors to readily get access to radiology reports that help improve diagnostic quality²². The company has a total of $3M in funding over 4 rounds. The investors include Crista Galli Ventures and EIT Health. The healthcare startup employs 28 people and is located in Denmark.

HelloSelf

Based in London, the startup provides AI-powered digital therapy services and matches therapists with users. It has $11.3 million in total funding including approximately €6.4 million in Series A from investors such as VC firm OMERS Ventures, Manta Ray Ventures, Oxford Capital Partners, and 2EnablePartners²³. The company founded in 2018 boasts 53 employees.

Acquisitions in 2021

AI-powered tools in the healthcare sector have also drawn significant attraction from the acquisition market in recent years. Here are the top five AI healthcare acquisitions according to 2021 Becker’s Hospital Review.

1) Microsoft acquired Nuance Communications

Microsoft acquired Machussetts-based Nuance Communications, in an all-cash transaction valued at $19.7 billion, including debt assumption²⁴. Microsoft plans to use the speech recognition company to expand its healthcare offerings for its cloud products. The company proposes tools for recognizing and transcribing speech in physician visits, voicemails, and more.

2) Olive acquired Empiric Health

Olive, a Columbus-based startup that provides artificial intelligence and robotic process automation solutions for healthcare organizations, has acquired Empiric Health, a Salt Lake City company that uses AI scans to capture thousands of data points for clinical analysis²⁵.

3) Mayo Clinic entered a deal with n/ference

Minnesota-based Mayo Clinic entered a partnership with AI-driven health technology company nference to launch Anumana Inc in view of creating and bringing to market innovative digital sensor diagnostics by applying nference AI to Mayo’s deep repository of medical data²⁶. Per the deal, Anumana will focus initially on designing state-of-the-art neural network algorithms based on billions of relevant pieces of heart health data in Mayo Clinic’s Clinical Data Analytics Platform, including raw electrocardiogram (ECG) signals, to unlock hidden biomedical knowledge and enable early detection as well as accelerate the treatment of heart disease.

4) Tegria bought KenSci

Seattle-based Tegria acquired KenSci, an artificial intelligence platform and application for healthcare, with roots in Microsoft’s Azure4Research program and the University of Washington, for an undisclosed value²⁷. Launched in 2015, this company provides an AI platform and solutions that help healthcare organizations modernize their workflows and power a digital, data-driven, value-based healthcare system.

5) Recuro landed MyLegacy

Dallas-based Recuro health, an integrated digital health solution, acquired MyLegacy, a risk stratification tool and clinical decision support application which uses proprietary algorithms based on practice guidelines developed by the Cleveland Clinic Genomic Medicine Institute²⁸.

Market Projections

AI technology is expected to experience a separate growth trajectory owing to this pandemic with surging market growth in a few artificial intelligence applications pertaining to the healthcare sector. Key participants are also expanding their portfolio to meet the demands in the current pandemic situation, which is also one factor depicting the surge in artificial intelligence penetration in healthcare applications. For instance, Qventus installed AI-based patient flow automation systems to help the healthcare facilities during this pandemic situation. The solution includes staying length optimization, ICU and med-surge capacity creation, COVID scenario planner, and critical resource control. Also, in May 2020, MIT — IBM Watson, AI Lab pushed artificial intelligence-based technology by funding 10 research projects that are aimed at addressing the economic and health consequences of the COVID pandemics.

Furthermore, the companies are increasingly focusing on expanding their geographical reach and introducing newer, innovative solutions through various strategies, including partnerships, product launches, and collaborations, to support the end-users in overcoming the shortage of radiologists, deliver value-based care, combat the COVID-19 pandemic by early disease detection and diagnosis, and maintain a competitive edge in the market.

The future of AI in health care could include tasks that range from simple to complex — everything from answering the phone to medical record review, population health trending and analytics, therapeutic drug and device design, reading radiology images, making clinical diagnoses and treatment plans, and even talking with patients.

The future of artificial intelligence in healthcare presents:

• A healthcare-oriented overview of artificial intelligence (AI), natural language processing (NLP), and machine learning (ML)
• Current and future applications in healthcare and the impact on patients, clinicians, and the pharmaceutical industry
• A look at how the future of AI in health care might unfold as these technologies impact the practice of medicine and health care over the next decade

References:

1. Butow, P. & Hoque, E. (2020). Using artificial intelligence to analyse and teach communication in healthcare. The breast. The Breast. doi:10.1016/j.breast.2020.01.008
2. 2. Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International journal of human–computer interaction. International Journal of Human–Computer Interaction. doi:10.1080/10447318.2020.1741118
3. Grand View Research. (2020). Artificial Intelligence Market Size & Share Report, 2020–2027. Retrieved October, from https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
4. Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of business analytics. Journal of Business Analytics. doi:10.1080/2573234x.2018.1543535
5. Yu, K.-H., Beam, A. L. & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering. Nature Biomedical Engineering. doi:10.1038/s41551–018–0305-z
6. https://www.bmj.com/content/353/bmj.i2139
7. https://www.accenture.com/_acnmedia/pdf-49/accenture-health-artificial-intelligence.pdf
8. https://www.reportsanddata.com/report-detail/artificial-intelligence-in-healthcare-market
9. https://medicalfuturist.com/top-artificial-intelligence-companies-in-healthcare/#
10. https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/
11. https://www.cbinsights.com/research/report/healthcare-trends-q1-2021/
12. https://craft.co/caption-health
13. https://www.beckershospitalreview.com/artificial-intelligence/healthcare-tops-100-most-innovative-ai-companies-8-digital-health-startups-on-the-list.html
14. https://www.beckershospitalreview.com/artificial-intelligence/healthcare-tops-100-most-innovative-ai-companies-8-digital-health-startups-on-the-list.html
15. https://craft.co/recursion-pharmaceuticals
16. https://craft.co/recursion-pharmaceuticals
17. https://craft.co/theator
18. https://craft.co/overjet
19. https://www.beckershospitalreview.com/artificial-intelligence/olive-raises-400m-hits-4b-valuation.html
20. https://craft.co/insitro
21. https://craft.co/owkin
22. https://www.crunchbase.com/organization/radiobotics
23. https://craft.co/helloself
24. https://www.beckershospitalreview.com/digital-transformation/what-microsoft-s-20b-nuance-acquisition-means-for-big-tech-s-healthcare-push-7-details.html
25. https://www.beckershospitalreview.com/artificial-intelligence/olive-acquires-intermountain-s-ai-clinical-analytics-spinoff.html
26. https://nference.ai/mayo-clinic-partnership
27. https://www.beckershospitalreview.com/artificial-intelligence/5-healthcare-ai-deals-made-in-2021.html
28. https://www.beckershospitalreview.com/artificial-intelligence/5-healthcare-ai-deals-made-in-2021.html