.com/healthcare
The Next Generation of Medicine:
Artificial Intelligence and Machine Learning
TM Capital
Industry Spotlight
Case Study: Growth Equity Capital Raise
Analytics 4 Life raised $ million through the sale of
Series B Convertible Preferred Stock
Analytics 4 Life, a Toronto-based developer of artificial
intelligence-enabled medical imaging solutions, has raised
$ million through the sale of Series B Convertible
Preferred Stock. Analytics 4 Life, led by former Sapheon,
Inc. CEO Don Crawford, is focused on using artificial
intelligence to improve, simplify and reduce the cost of
diagnosing coronary artery disease (“CAD”), a $6 billion
global market. The Company’s non-invasive medical
device, CorVista™, applies machine learned solutions to
assess the presence of significant CAD, using
physiological signals naturally emitted by the body.
Beyond CAD, Analytics 4 Life has begun to apply its
proprietary signal processing and artificial intelligence
platform to developing new products that address other
cardiac conditions and disease states in neurology and
oncology.
TM Capital served as financial advisor to Analytics 4
Life in connection with this transaction. The Company
plans to use the proceeds to complete the final stage of
testing and apply for FDA approval. In addition, the
financing will enable Analytics 4 Life to build the necessary
team to bring this potentially transformative diagnostic
solution to a wide audience of physicians and patients.
1
2
Introduction
Artificial Intelligence (“AI”) applications, powered by an influx of big data and advancements in computing power, are
positioned to transform major sectors, while simultaneously creating new industries. AI is expected to contribute up to $
trillion to global GDP by
The AI industry has the capability to not only augment and improve, but also to replace many tasks that have been
historically executed by humans. Simultaneously, AI will create many new jobs that are yet to be identified. According to
the . Department of Labor, “65% of the school children [in 2016] will be eventually employed in jobs that have yet to be
created.” New technologies and innovations in AI will transform most consumer, enterprise and government markets around
the world. However, the commercial uses for AI applications are still nascent and ripe for investment. As such, the industry
is attracting strong interest from a broad range of investors.
This report will review the important role that AI plays in healthcare, but first we will summarize the definition of AI and its
evolution to date.
Defining Artificial Intelligence and Machine Learning
AI refers to multiple technologies that can be combined in
different ways to sense, comprehend and act with the
ability to learn from experience and adapt over time (See
Figure 1). In basic terms, AI is a broad area of computer
science that makes machines and computer programs
capable of problem solving and learning, like a human
brain. AI includes Natural Language Processing (“NLP”)
and translation, pattern recognition, visual perception and
decision making. Machine Learning (“ML”), one of the most
exciting areas of AI, involves the development of
computational approaches to automatically make sense of
data – this technology leverages the insight that learning is
a dynamic process, made possible through examples and
experiences as opposed to pre-defined rules. Like a
human, a machine can retain information and becomes
smarter over time. Unlike a human, a machine is not
susceptible to sleep deprivation, distractions, information
overload and short-term memory loss – that is where this
powerful technology becomes exciting.
The Evolution of AI and ML
AI is not a new concept – in fact, much of its theoretical and technological underpinning was developed over the past 60
years. Although AI has been a part of our day-to-day lives for some time, this technology is at an inflection point, largely
due to major recent advances in deep learning applications. Deep learning is a sub-set of ML that utilizes networks which
are capable of unsupervised learning from data that is unstructured or unlabeled. The neural networks that underpin deep
learning capabilities are becoming more efficient and accurate due to two significant recent technological advancements:
an unprecedented access to big data and an increase in computing power. The effectiveness of neural networks correlates
1 PwC, “AI to drive GDP gains of $ trillion with productivity, personalisation improvements” (June 27, 2017)
Figure 1: What is AI and ML?
The AI industry – encompassing a broad set of information systems inspired by human learning and
reasoning systems – is a $ billion market that is expected to grow dramatically to over $59 billion
by 2025. The Healthcare AI market, among the AI industry’s fastest growing sub-sectors, is expected
to grow at a % CAGR to over $10 billion in worldwide revenue by 2024.
Artificial Intelligence refers to multiple
technologies that can be combined to:
AI
T
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ol
og
ie
s
Act Sense
Machine
Learning
Language
Processing
Computer
Vision
Expert
Systems
Knowledge
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Audio
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Source: Accenture, “Why is AI The Future of Growth” (2016).
3
to the amount of data available. With the influx of innovations such as
mobile devices, more data is available than ever before, with annual
data generation expected to grow at a 141% CAGR over the next five
,3
While some AI applications have been implemented across many
industries, transformative commercial uses are still young. AI adoption
outside of the tech sector is, in many cases, at an experimental stage.
In McKinsey’s survey of 3,000 AI-aware C-level executives, across 10
countries and 14 sectors, only 20% said they currently use any AI-
related technology at scale or in a core part of their businesses. A review
of more than 160 use cases shows that AI was deployed commercially
in only 12% of
Companies and financing sources with the resources to invest in AI are
placing their bets on emerging AI companies and technologies, creating
a catalyst for others to do the same or risk missing the boat on this
opportunity. According to a study by Cowen and Company, 81% of IT
leaders are currently investing or planning to invest in AI, while 43% are evaluating and pursuing proof of In 2016,
despite a decline in venture capital funding across industries overall, AI startups raised a record $5 billion, a 71% CAGR
since Fueled by these significant investments, the worldwide AI market is expected to grow at a 52% CAGR to over
$59 billion by 2025 (See Figure 2).
AI & ML Driving Industry Transformations
With applications in almost every industry, AI promises to significantly transform existing business models while
simultaneously creating new ones. In financial services, for example, there are clear benefits from improved accuracy and
speed in AI-optimized fraud-detection systems, forecast to be a $3B market in In 2025, algorithmic trading strategy
performance improvement and static image recognition, classification and tagging are predicted to be the top revenue-
generating applications of AI across world markets (See Figure 3).
Figure 3: AI Revenue, Top 10 Use Cases, World Markets – 2025
2 IBM, “10 Key Marketing Trends for 2017” (2017)
3 IDC, “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things” (April 2014)
4 McKinsey Global Institute, “Artificial Intelligence the Next Digital Frontier?” (June 2017)
5 Forbes, “How Artificial Intelligence is Revolutionizing Enterprise Software in 2017” (June 2017)
6 CB Insights, “The 2016 AI Recap: Startups See Record High In Deals and Funding” (January 2017)
Figure 2: Worldwide AI Market Revenue
$766
$890
$1,014
$1,089
$1,137
$1,175
$1,369
$2,260
$2,321
$2,396
— $500 $1,000 $1,500 $2,000 $2,500 $3,000
Contract analysis
Object detection and classification - avoidance, navigation
Object identification, detection, classification, tracking from geospatial images
Automated geophysical feature detection
Text query of images
Content distribution on social media
Predictive maintenance
Efficient, scalable processing of patient data
Static image recognition, classification, and tagging
Algorithmic trading strategy performance improvement
($ in mllions)Source: Tractica, "Artificial Intelligence Market Forecasts" (September 2016).
4
AI’s self-learning capabilities coupled with tools like data
mining, pattern recognition and NLP will allow it to eventually
mimic human-like behavior – developing common sense
reasoning and opinions. The key advantages of AI over
human intelligence are its scalability, longevity and
continuous improvement capabilities. Such attributes are
anticipated to dramatically increase productivity, lower costs
and reduce human error. Although at a nascent stage, this
technology is likely to introduce a new standard for corporate
productivity, competitive advantage and, ultimately,
economic growth.
AI and ML applications have implications for disruption
across every industry. The healthcare industry is expected
to benefit from $45 billion in annual cost savings by 2025,
followed closely by the finance industry’s $34 to $43 billion
in annual cost savings and new revenue and the retail
industry’s $41 billion in annual new revenue (See Figure 4).
AI in Healthcare
Evolution of AI in Healthcare
Healthcare is one of the largest and most rapidly growing segments of AI, driven predominantly by innovation
in clinical research, robotic personal assistants and big data Healthcare is poised to accelerate
investments in AI over the next three years (See Figure 5). The influx of healthcare data has resulted in a
growing need for AI technology to enhance data mining and computing capabilities. Personalized treatments
are being bolstered by growing application of AI in the field of genomics and precision medicine. The emergence of new
and promising applications for disease diagnosis and monitoring is anticipated to further drive AI market growth.
Figure 5: Sectors Leading in AI Adoption Technology Also Intend to Grow Investments Most Rapidly
7 Global Market Insights, “Healthcare AI Market Size, Competitive Market Share & Forecast, 2024” (2017)
Figure 4: Artificial Intelligence Ecosystem
AI – Opportunity Size by Major Industry (2025E)
Source: Goldman Sachs, “Profiles in Innovation: AI” (November 14, 2016).
$45 billion
annual cost savings
HEALTHCARE
$34 - 43 billion
annual cost savings &
new revenue
FINANCE
$20 billion
total addressable market
AGRICULTURE
$41 billion
annual new revenue
RETAIL
Travel and Tourism
Professional ServicesConstruction
Education Healthcare
Retail
Consumer
Packaged Goods
Transportation and LogisticsMedia and Entertainment
Energy and Resources
Automotive and Assembly
High Tech and
Telecommunications
Financial Services
—
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20
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Future AI Demand Trajectory
Average estimated % change in AI spending,
next 3 years, weighted by firm size
Source: McKinsey Global Institute AI Adoption and Use Surv ey ; McKinsey Global Institute Analy sis
5
In the ., favorable government dynamics and
the adoption of big data analytics continue to drive
industry growth. The government seeks to reduce
costs and improve quality of healthcare services
with data analytics and AI initiatives. The .
healthcare AI market exceeded $320 million in
2016, and is estimated to grow by more than a 38%
CAGR through The global healthcare AI
market is expected to grow at a % CAGR to
over $10 billion by 2024 (See Figure 6).
Key Growth Drivers in the Healthcare AI Market
There is an increasing need for healthcare
organizations to implement solutions that
effectively improve treatment outcomes,
manage rising costs and navigate through the
demands confronting the sprawling healthcare
Startups are leveraging ML
algorithms to help solve key problems such as
reducing time and error costs in the drug
discovery process, providing virtual assistance
to patients and improving accuracy of
diagnosis with medical imaging and diagnostic
procedures.
The primary macroeconomic growth drivers of AI in
healthcare include increasing individual healthcare
expenses, a larger geriatric population and an
imbalance between health workforce and patients (See
Figure 7).8,10 Global expenditures on healthcare
increased to % of total GDP in 2014, up from %
in The US witnessed the highest expenditure on
healthcare, % of total GDP, in The world’s
population, aged 60 years and above, is likely to grow
by 56% from 2015 to The shift towards an aging
population will strain the current healthcare
Because of these trends, the . has a continuous
shortage of nursing and technician staff. The number of
vacancies for nurses will be million by AI is
positioned to help medical practitioners efficiently
achieve their tasks with minimal human intervention, a
critical factor in meeting increasing patient demand.
8 Global Market Insights, “Healthcare AI Market Size, Competitive Market Share & Forecast, 2024” (2017)
9 Managed Healthcare Executive, “Top 2017 Challenges Healthcare Executives Face” (December 8, 2016)
10 Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group; US Department of Commerce, Bureau of
Economic Analysis; and National Bureau of Economic Research Inc.
Figure 6: Global Healthcare AI Market Growth Through 2024
Figure 7: Key Growth Drivers in the Healthcare AI Market
Industry Growth Drivers8,10
Adoption of AI in research areas
Increasing range of future applications
Reduced workload and increased quality of care
Growing demand for precision medicine
Growing number of cross-industry partnerships
Shortage of health workforce to meet patient demand
Need to reduce increasing healthcare costs
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Key Adoption Challenges in the Healthcare AI Market
The investment marketplace’s growing appetite for AI suggests
strong expectations for this highly anticipated technology to
produce exceptional breakthroughs in healthcare. However, this
innovation continues to be challenged by inherent factors in the
healthcare market, making the road to full AI integration difficult
(See Figure 8). State and federal regulators are a key hurdle
facing AI and ML integration, while regulators juggle the balancing
act between the advantages and disadvantages of the technology.
Data privacy regulation will likely be at the forefront of this battle.
While AI’s development costs, integration and the
fear of replacing humans in the workplace are
among key challenges for AI, one of the greatest concerns facing
the industry is the response from regulators. Certificates of Need,
risk-based capital requirements and burdensome reporting can
create major barriers to new entrants and Mobile
health applications and devices that use AI pose a new regulatory
challenge for the . Food and Drug Administration (FDA).
“We're trying to get people who have hands-on development
experience with a product's full life cycle,” says Bakul Patel, the FDA’s associate director for digital health, “we already have
some scientists who know AI and ML, but we want complementary people who can look forward and see how this technology
will evolve.”14 To address this issue, the agency has formed a central digital health unit within its Center for Services and
Radiological Health, assembling a team of engineers and computer scientists to help anticipate and oversee future
developments in AI-driven medical The team will be responsible for understanding how ML, AI and related
subjects will affect healthcare in the The new unit will also assure that the regulatory process can accommodate the
rapid and iterative process of software updates commonly used to improve existing products and
11 Global Market Insights, “Healthcare AI Market Size, Competitive Market Share & Forecast, 2024” (2017)
12 Markets and Markets, “Artificial Intelligence in Healthcare Market” (2017)
13 Cardiogram, “Three Challenges for Artificial Intelligence in Medicine” (September 19, 2016)
14 IEEE Spectrum, “FDA Assembles Team to Oversee AI Revolution in Health” (May 29, 2017)
Figure 8: Potential Challenges in the Healthcare
AI Market
Industry Challenges11,12
High initial capital requirement11
Potential for increased unemployment
Difficulty in deployment
Reluctance among medical practitioners to adopt
AI12
Ambiguous regulatory guidelines for medical
software
Lack of curated healthcare data
Concerns regarding privacy and security
Lack of interoperability between AI solutions
State and Federal Regulations
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AI’s Role in the Changing Healthcare Job Market
There is little doubt that AI will change the healthcare job
market, just as the advent of new technology reshaped the
professional landscape during the Industrial Revolution. As
large companies race to expand their AI capabilities, the
prospect of AI’s role within the healthcare industry raises a new
question – will doctors and caregivers lose jobs to AI?
While these concerns are valid, AI
companies, such as Infervision, that use
deep learning and computer vision to
diagnose cancers, market themselves as “An Extra Pair of
Eyes.” Infervision’s CEO, Chen Kuan, insists the technology is
“intended to eliminate much of the highly repetitive work and
empower doctors to help them deliver faster and more accurate
reports.”15 The reality of the situation, industry expert Sebastian
Thrun (Stanford University’s former director of the Artificial
Intelligence Laboratory) notes, is that “deep-learning devices
will not replace dermatologists and radiologists. They will
augment the professionals, offering them expertise and assistance.”16 Robots and computer programs have already begun
to replace personnel and staffing in medical facilities, particularly in administrative functions, by managing wait times and
automating scheduling processes. With the physician shortage expected to double within the next nine years, AI can
address an estimated 20% of unmet clinical demand by automating certain tasks and enhancing efficiency, quality and
patient outcomes (See Figure 9).
15 Forbes, “See How Healthcare Artificial Intelligence Can Improve Medical Diagnosis and Healthcare” (May 16, 2017)
16 The New Yorker, “. Versus .” (April 2017)
Figure 9: AI Will Address Unmet Clinical Demand
—
2016 2021 2026
U.
S.
C
LI
NI
C
IA
NS
Source: Accenture Analy sis. Graph is illustrativ e and not to scale
Clinician
Demand
20%
Estimated
Unmet
Demand
Addressable
via AI
Clinician
Supply
Case Study: Big Pharma Investment in Healthcare AI Start-up
Tencent leads $155 million series A investment in iCarbonX
XXXX
Case Study: Tech Giant Investment in Healthcare AI Start-up
Tencent leads $155 million Series A investment in iCarbonX
iCarbonX’s “Digital Life Alliance”
In June 2016, China-based Tencent Holdings (SEHK: 700), one of the
largest internet and gaming companies in the world, led a $155 million
series A funding round in iCarbonX, an AI-enabled health data mining start-
up.
Founded in October 2015, iCarbonX has raised over $600 million at more
than a $1 billion valuation, making it one of the youngest tech unicorns
(start-up company valued over $1 billion). According to iCarbonX CEO Jun
Wang, “after completing this round of funding, iCarbonX will develop the
following four areas: nutrition, health, medical treatment and cosmetics.”
iCarbonX, using machine learning algorithms, analyzes genomic,
physiological and behavioral data to provide customized health and
medical advice directly to consumers through an app, Meum™. Released
to the public in January 2017, the digital health management platform uses
reams of health data to provide customized medical advice.
iCarbonX has formed alliances with leading health technology and application
companies around the world which specialize in gathering different types of
healthcare data. Together, these companies are working to help people better
understand the medical, behavioral and environmental factors in their lives that
may accelerate or mitigate disease and optimize health.
Tencent, like many tech giants, has taken a targeted interest in the healthcare
AI space, investing in a number of health-related AI startups – including mobile
medical device startup Scanadu, smart digital body fat scale company Picooc
and Guahao, a medical services platform.
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AI Healthcare Industry Breakdown
AI and ML applications are driving improvements across five
primary areas of healthcare: Intelligent Diagnostics, Patient
Provider Data Management, Drug Discovery Process with
Advanced Analytics, Medical Devices & Robotics, and Home Health
(See Figure 11). Over the past five years, drug discovery has led
the healthcare AI application market. Holding 35% of the global AI
market share, drug discovery AI applications are anticipated to
exceed $4 billion by 2024 (See Figure 10). Within this segment, AI
capabilities to discover, identify and screen molecules instantly and
effectively are becoming ever more effective. The table below
depicts examples of AI companies within each of the key healthcare applications:
Figure 10: AI Healthcare Industry (2024)
Figure 11: Key Healthcare Applications of AI
$
$
$
Intelligent Diagnostics
Drug Discovery
Other
($ in billions)
Source: Global Market
Insights
9
Intelligent Diagnostics
Approximately 12 million Americans are misdiagnosed in outpatient clinics In today’s stringent
healthcare compliance environment, practitioners face increasingly high stakes in events of misdiagnosis and
mistreatment.
Patient medical history, genetic predispositions, treatment history and other factors converge in complicated ways that often
make practitioners’ jobs difficult. AI in healthcare aims to close the diagnosis veracity gap. AI-powered diagnostic systems
are built with algorithms that can make sense of large sets of patient data, recognize patterns and spot relationships to
ultimately arrive at a clinical decision. With the help of ML, the more data these technologies have access to, the smarter
they become, leading to continually increasing capabilities, scalability, efficiency and accuracy. While early in development,
AI’s potential impact has already significantly influenced the imaging and diagnostics space. Research centers are now
applying their expertise in AI and ML to improve diagnosis and treatment. Frost & Sullivan recently reported excellent
treatment and patient outcomes quantified by the results of several studies exploring the impact of AI and ML as a decision
support tool in
Under the traditional pathway of diagnosis, practitioners are forced to make critical decisions solely based on their own
ability to compare the visuals of thousands of medical images. Now, technologies like IBM’s Watson are learning to
recognize patterns in imaging and text in electronic health records to make accurate diagnosis of breast and cardiac
specialties easily repeatable. As these systems continue to grow in penetration, harmful oversights and misdiagnoses will
be minimized while patient treatment will be more easily streamlined and customized. With the presence of AI in the
healthcare system, the scope of its impact will only expand – every type of medical specialty will be able to increase the
accuracy of diagnosis with the assistance of such
17 CBS News, “12 Million Americans Misdiagnosed Each Year” (2014)
18 Frost & Sullivan, “Transforming Healthcare Through Artificial Intelligence Systems” (2016)
19 W3R, “How Artificial Intelligence in Healthcare Will Be a Game Changer” (January 30, 2017)
Case Study: Intelligent Diagnosis Startups – Pathway Genomics, Bay Labs and Frenome
Area of Focus:
Cancer Diagnostics
Pathway Genomics, founded in 2008, is a medical diagnostic company with
mobile applications designed to empower physicians and patients to take
control of their health and wellness. The Company, based in San Diego,
offers genetic testing to support treatment of a variety of health conditions,
including hereditary cancer, diabetes, hypertension, cardiac conditions, drug
response and more.
In 2014, the Company announced its partnership with IBM Watson to help
“deliver the first-ever cognitive consumer-facing app based on genetics from
user’s personal makeup.” The mobile app, OME™, combines genetics, test
results, health records and wearables with evidence-based and wellness
knowledge to deliver to the user tailored and actionable recommendations
for general health and fitness.
This venture capital-backed company has raised $45 million since its
inception from investors, including IBM Watson Group and The Founders
Fund.
Freenome, a two year old liquid biopsy diagnosis platform, has developed a solution that applies ML for early-stage disease detection. The
Company’s goal, according to CEO Gabe Otte, is “to bring accurate, accessible and non-invasive disease screenings to doctors to proactively treat
cancer and other diseases at their most manageable stages.”
Freenome has garnered significant interest from some of the most active healthcare AI investors. The Company recently raised $65 million in Series
A funding led by Andreessen Horowitz, the same VC firm that led Freenome’s $ million seed round less than a year ago. Other investors in the
most recent financing include GV, Polaris Partners, Innovation Endeavors, Spectrum 28, Asset Management Ventures, Charles River Ventures and
AME Cloud Ventures.
Area of Focus:
Cancer Diagnostics
Founded in 2013, Bay Labs, has developed software for the
diagnosis of rheumatic heart disease using deep learning
technology. The Company develops technology that simplifies
the video recording, editing and sharing process, using
intelligent video analysis and user modeling technologies.
Bay Labs’ purpose is to assist practitioners in interpreting
ultrasound images of the heart faster and more accurately. Bay
Labs deploys AI software that, through repetition, is trained to
comprehend the results of ultrasound images. This non-
invasive solution can operate 20 times faster and reach eight
times more people than traditional diagnostic scanning methods
and costs % of the price.
Bay Labs is working with a network of world-class clinical and
academic advisors as well as leading VC firms. The Company’s
partnerships include Allina Health, Minneapolis Heart Institute
and the National Science Foundation. Bay Labs has raised over
$7 million in VC-funding from Khosla Ventures and Data
Collective, among others.
Area of Focus:
Cardiac Diagnostics
10
Dr. Joseph Reger, CTO of Fujitsu EMEIA said, "AI is now disrupting how businesses operate and will change the way that
organizations create real value for the customer or patient. Industries can reap huge benefits by developing cooperative
models that can quickly combine businesses needs with AI tech."20 Research anticipates clinical support from AI has the
potential to improve diagnostic outcomes by 30% to 40%, while reducing treatment costs by 50%.21 By driving a significant
improvement in misdiagnosis rates, AI systems have the potential to reduce hospital stays, unnecessary testing and health
care costs, signifying a new era in medicine.
The imaging & diagnostics sector is among the most active areas in the healthcare AI industry. According to CB Insights,
of the 50 healthcare AI-focused startups that have raised their first round of funding since January 2015, one third compete
in the imaging & diagnostics The market has seen AI and ML-enabled diagnostic technologies focusing on
applications such as mental health, coronary disease, cancer and even patient wearables. The table below represents the
landscape of companies, both established and start-ups, that have emerged in the healthcare AI diagnostics space:
Figure 12: Representative Healthcare AI Diagnostics Landscape
20 Forbes, “See How Healthcare Artificial Intelligence Can Improve Medical Diagnosis and Healthcare” (May 16, 2017)
21 Frost & Sullivan, “AI & Cognitive Computing Systems in Healthcare” (December 9, 2016)
22 CB Insights, “AI in Healthcare Heatmap: From Diagnostics to Drug Discovery Startups, the Category Heats Up” (September 16, 2016)
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Patient and Provider Data Management
The amount of “big data” available that can help
drive AI applications across global healthcare
organizations today is compounding and has
reshaped the industry, permeating every
component of the healthcare system. The global
Electronic Health Records (“EHR") market size alone was
estimated at around $21 billion in Big data and emerging
analytical solutions have grown exponentially in sophistication and
adoption in the last decade as healthcare providers turned to EHRs,
digitized laboratory slides, and high-resolution radiology images.
In addition, a vast amount of data exists in health insurance
company claims databases, pharmaceutical and academic
research archives and data streaming from wearable
Although more data exists than ever before (See Figure 13), nearly 80% of health data is unstructured and difficult to
Given that data continues to live in silos, providers are unable to effectively leverage this data to achieve
actionable guidance and in many cases still struggle to understand how big data is so critical for population health
management, value-based care, and other upcoming challenges. “There’s no room anymore for inconsistent quality and
inconsistent data,” said Ann Chenoweth, MBA, RHIA, FAHIMA, President and Chair Elect of the 2017 AHIMA Board of
Directors. Chenoweth points out that, “Trusted data must be reliable, accurate, and accessible, where and when it’s needed.
It’s not the data that comes out of here versus the other system. It must be an enterprise-wide framework that you can rely
on. Having that integrity and governance around the data is key.”
Improving the quality of care requires a broad base of data analysis and predictive analytics
that can support clinical decision-making. As health data continues to become more
accessible, significant opportunity exists for AI and ML to not only remove costs associated
with procedural tasks but also improve care via deep learning and algorithmic programing. AI-powered platforms, such as
CloudMedx, are now deploying healthcare specific NLP and ML technologies to generate real-time clinical insight with the
goal of improving patient management, clinical outcomes and reducing unnecessary costs. CloudMedx combines evidence-
based algorithms and big data architectures to make sense of the structured and unstructured data that are stored across
multiple clinical workflows, allowing it to provide actionable guidance and improve value-based As an example, the
CloudMedx platform combines standard clinical algorithms with AI to capture social, economic and clinical factors that
predict patient behavior and prompt interventions at appropriate times to avoid costly readmissions.
There is a need for tools that will allow healthcare organizations to understand how much data they have, how useful it is
for advanced analytics and in which use cases the data can be leveraged. When searching for vendors offering data-driven
solutions that address existing data challenges, providers will turn to products that are scalable, easily integrated into their
existing infrastructure and maximize the value of historical data AI & ML-enabled technologies are estimated to be
capable of driving efficiencies in healthcare information worth more than $28 billion per year
23 Grand View Research, “Electronic Health Records Market Size” (April 2017)
24 GE Healthcare, “Big Data, Analytics & AI: The Future of Health Care is Here” (2016)
25 Healthcare Data Institute, “Big Unstructured Data’s Contribution to Healthcare” (February 18, 2015)
26 NewsCenter, “Software Startup CloudMedx Secures $ Million” (August 15, 2017)
27 Health IT Analytics, “How Healthcare Can Prep for Artificial Intelligence, Machine Learning” (2017)
28 Goldman Sachs, “Profiles in Innovation: Artificial Intelligence” (November 14, 2016)
Figure 13: Global Healthcare Data Collected
12
Drug Discovery Process with Advanced Analytics
The extensive drug development and approval process is a primary source of cost in a healthcare environment
where pharmaceutical R&D spend is on the rise. Deloitte recently estimated R&D spend to reach $162 billion by
2020, a troubling estimation challenging the industry’s balancing efforts between growing innovation and
containing Furthermore, the inefficiencies in the current drug development process carries significant exposure to
statistical error posing substantial financial risk for large pharmaceutical companies.
According to the California Biomedical Research Association, under the current methods of the drug development and
approval process in the ., it takes an average of 12 years for a drug to travel from the research lab to the patient (See
Figure 14). Only five in 5,000 (or %) of the drugs that begin pre-clinical testing ever make it to human testing and just
one of these is ever approved for human usage. Furthermore, on average, it will cost a company $359 million to develop a
new drug from the research lab to the patient. 30
Drug research and discovery is a recent, but rapidly growing application for AI and ML in healthcare. These tools offer
improved efficiency and accuracy at all stages of the discovery process.
29 Deloitte, “2016 Global Life Sciences Outlook” (2015)
30 PwC, “What Doctor? Why AI and Robotics Will Define New Health” (June 2017)
Figure 14: Drug Development Process in the United States
Basic Science
Research
Preclinical
Testing
Clinical
Trials
Government
Approval Approved
Drug
2-5 Years 1-2 Years 5-7 Years 1/2-2 Years
OncoEMR, Flatiron’s electronic medical
record product – optimizes clinical
decision making and reduces costs
Case Study: Big Pharma Investment in Healthcare AI Start-up
Roche leads $175 million Series C investment in Flatiron Health
New York healthcare IT startup Flatiron Health, which is focused on accelerating cancer research and optimizing
therapies based on patient data, completed a $175 million Series C financing in 2016, led by Roche
Pharmaceuticals – Allen & Co. and Casdin Capital also participated in the round. Flatiron has raised $313 million
in aggregate funding, including investments from GV and First Round Capital. The Company is taking a data
driven approach to cancer, using machine learning to extract information from patients’ electronic health records,
which it then gathers into software solutions. Founded by former Google employees Nat Turner and Zac
Weinberg, Flatiron plans to use this funding to further enhance its cloud-based software platform that will help
cancer care providers maximize efficiency, identify revenue opportunities and better engage with patients.
In parallel with the financing round, Flatiron Health will enter into a non-exclusive agreement with
Roche, in which the pharma giant will purchase a number of Flatiron’s software products and
collaborate on R&D to accelerate clinical trials, advance personalized medicine and enhance patient
care. Roche, the world’s largest biotech company and a leader in oncology diagnostics, plans to use
Flatiron’s data to bring new drugs to market faster, keeping its drug pipeline full of innovative new
treatments. The deal highlights corporate investors’ continued interest in artificial intelligence and
willingness to provide financing to gain access to start-up expertise. Roche COO Daniel O’Day noted
“Flatiron has tremendous data that helps us understand how medicine reacts to patients – this is a
long-term strategic investment.” Doctors treating more than million cancer patients in the US –
one in eight of all people diagnosed with cancer – use Flatiron Health’s products; the Company
believes that it can continue to transform how cancer centers, pharma companies and other
healthcare professionals create faster treatments and pair patients with more effective regimens.
13
AI and ML-driven technologies have begun to show their benefit across multiple labs worldwide. In
February 2017, Boston-based startup BERG Health announced that their AI platform had selected a
drug candidate for rare brain cancers that then entered clinical trials as a monotherapy (or a stand-
alone treatment). BERG Health’s AI-based Interrogative Biology Platform guided this drug candidate through an early
development process by analyzing data from thousands of cancer patients to build an in silico (computer simulated) disease
model and suggest possible drug BERG Health’s President and co-founder believes that this AI platform
reduces the traditional platforms’ cost and time in half, stating, “We’ve essentially reversed the scientific method…Instead
of a preconceived hypothesis that leads us to do experiments and generate a particular type of data, we allowed the
biological data from the patients to lead us to the hypothesis.”31
A key differentiator between an AI-enabled and a traditional drug discovery process is that AI replaces the need for a human
to make a hypothesis – AI enables the use of patient-derived data to generate Researchers apply AI to the
drug discovery process to investigate biological systems and understand a drug’s effect on a patient’s cell or tissue. The
biological insight driven by ML can assist pharmaceutical companies to better identify and recruit patients for clinical trials
of therapies for diseases to which they are highly susceptible, reducing attrition rates and increasing the probability of
receiving FDA approval. The implications of AI & ML capabilities have been estimated to potentially provide the
pharmaceutical industry with nearly $27 billion in development cost savings globally by Taking full advantage of this
revolution, a multitude of the most notable names in the pharmaceutical landscape have begun investing in these disruptive,
AI & ML-enabled drug discovery technologies.
31 Wired, “The Startup Fighting Cancer with AI” (March 22, 2016)
32 Wall Street Journal, “How AI is Transforming Drug Creation” (June 25, 2017)
33 Harvard University, “Make the FDA Great Again? Trump and the Future of Drug Approval” (March 22, 2017)
Established Pharmaceutical Companies Partnering with Niche Players in the Healthcare AI Market
In December 2016, IBM announced that Pfizer would
be one of the first organizations to utilize the “Watson
for Drug Discovery” cloud-based platform to help
accelerate Pfizer's research in immuno-oncology, an
approach to cancer treatment that uses the body's
immune system to help fight cancer.
IBM Watson is an AI cloud-based system capable of
processing high volumes of data and offers evidence-
based answers to questions posed in natural
language. The collaboration targets cancer therapies
and aims to “help life sciences researchers discover
new drug targets and alternative drug indications.”
Pfizer will be among the first to leverage Watson’s
cloud-based cognitive tool composed of machine
learning, natural language processing (NLP) and other
cognitive reasoning technologies to support the
identification of new drug targets, combinations of
therapies for study and patient selection strategies in
immuno-oncology.
According to Mikael Dolsten, President of Pfizer
Worldwide Research & Development, "Pfizer remains
committed to staying at the forefront of immuno-
oncology research…With the incredible volume of
data and literature available in this complex field, we
believe that tapping into advanced technologies can
help our scientific experts more rapidly identify novel
combinations of immune-modulating agents. We are
hopeful that by leveraging Watson's cognitive
capabilities in our drug discovery efforts, we will be
able to bring promising new immuno-oncology
therapeutics to patients more quickly."
In June 2017, Genentech, a member of the Roche group, announced a collaboration with
GNS Healthcare, a precision medicine company focused on cancer therapy. The
companies aim to use machine learning to convert high volumes of cancer patient data into
computer models that can be used to identify novel targets for cancer therapy.
In December 2014, Roche acquired Bina Technologies, a biotech company targeting the
personalized medicine sector by providing a platform for large-scale genome sequencing.
In addition, Roche is among a handful of initial pharmaceutical partners for BERG Health,
a Boston-based startup company focused on applying AI drug development and discovery
and healthcare diagnostics.
GlaxoSmithKline (“GSK”) announced a $43 million deal with UK-based AI firm Exscientia
to enhance its drug discovery process. Exscientia will apply its AI-enabled drug discovery
platform, in combination with GSK’s expertise, to discover selective small molecules for
up to ten disease-related targets, identified by GSK, across multiple therapeutic areas.
GSK also partnered with IBM Watson to use artificial intelligence to market its Theraflu®
cold and flu medication. Watson’s AI-powered technology, Watson Ads, enables
customers to engage with the company’s advertisements by empowering people to ask
questions via voice or text right through GSK’s online ads.
Tony Wood, Pfizer’s previous Senior Vice President of Medicinal Sciences, has been
appointed Senior Vice President, Platform Technology and Science, Pharma R&D at GSK
– Wood is expected to assume his position in October 2017. Wood will take over for John
Baldoni, who will lead a new team focused on “enhancing drug discovery through the use
of in silico technology — including artificial intelligence, machine learning and deep
learning.”
14
Medical Devices & Robotics
A vast opportunity exists for AI to transform the field of surgical robotics through devices that can perform semi-
automated surgical tasks with increasing efficiency. Robotics have already proven their effectiveness in helping
hospitals save costs, reduce waste, and improve patient care. It is estimated that the market for healthcare
robotics will grow at a 21% 5-year CAGR to $ billion by 2021, with surgical robots comprising the largest
AI and ML applications can transform surgical robots from programmable machines, as they currently exist,
into smart assistants. Robotics outcomes have been shown to decrease a patient’s length of stay by nearly 21% and, when
combined with AI & ML capabilities, are poised to enhance the effectiveness, safety, consistency and accessibility of surgical
Recent advances in sensors, combined with AI and ML, are paving the way for real-world
applications of intelligent robotics in medicine. Surgery is transitioning into a digitized era
and cutting-edge technology companies are working to stay at the forefront. In 2015,
Google’s Verily Life Sciences partnered with Johnson & Johnson’s (“J&J”) Ethicon, a
medical device company, to further advance medical robotics. The newly formed joint
venture, Verb Surgical, leverages Ethicon’s expertise in surgical instrumentation and
Google’s capabilities across machine vision, imaging analysis, and data Google and J&J aim to complement
surgeons’ abilities, using AI, by enabling identification of critical body structures which, in many cases, are undetectable by
a screen on the tissue
In Q1 2017, Verb Surgical announced it successfully created its first digital surgery prototype, featuring five core technology
elements including robotics, visualization, advanced instrumentation, data analytics, and connectivity. Gary Pruden,
Executive VP and Worldwide Chairman of Medical Devices at J&J, added, “The team has made important progress since
Verb was formed in August 2015. The digitally-enabled surgery platform is a great example of how healthcare can be
transformed through innovation.”36
Founded in Caesarea, Israel, Medical Surgical Technologies (“MST”), is also at
the apex of deploying intelligent medical robotics powered by AI and advanced
analytics. MST’s initial offering, AutoLap™, is an image-guided robotic
laparoscope positioning system used across general laparoscopic, gynecologic,
and urologic procedures. At the core of MST’s AutoLap™ lies an imaging-
analysis software which allows the laparoscope positioning system to provide
real-time guidance, thus stabilizing the surgeon’s motions during an operation.
From an economic standpoint, AutoLap is cost-effective; significant training is
not required and the system can integrate seamlessly with current standard
surgical
How does it work? In the operating room, the surgeon wears a wireless ring-shaped device that interacts with the AutoLap
system. The embedded proprietary software collects and analyzes visual data and maneuvers the laparoscope in line with
the surgeon’s actions during the
MST’s advanced software, Follow Me™, received FDA clearance in 2016. In an interview with ISRAEL21c, MST CEO Motti
Frimer stated, “We are addressing a real need in computer-assisted robotic surgery, because most robotics must be
commanded by joysticks or other devices while the MST image-analysis platform responds to the surgeon’s actions. We
aim to be the gold standard for all laparoscopic surgery, and also hope to expand MST’s image-based AI technology into
additional medical robot and computer-assisted surgical domains.”
34 Markets and Markets, “Medical Robots Market Worth Billion USD by 2021” (2017)
35 Accenture, “Artificial Intellignece: Healthcare’s New Nervous System” (2017)
36 Med Device Online, “J&J-Google Joint Ventue Verb Surgical Unveils Digital Surgery Prototype To Partners” (2017)
37 Johnson & Johnson, “Johnson & Johnson Announces Definitive Agreement To Collaborate With Google To Advance Surgical Robotics”
38 ISRAEL21c, “7 Israeli robots that are transforming surgery”
39 MST Medical Surgery Technologies Ltc., Company Website
15
Home Health using Artificial Intelligence
In addition to the operating room, AI improvements to robotics can transform the home health and end-of-life
care sector. Advances in NLP and social awareness algorithms have already begun to make social robots
dramatically more useful to consumers as companions or personal assistants. In an industry characterized by
increased demand from aging populations and a deficit of care workers, the global personal robot market,
including smart “care-bots”, is expected to grow to $ billion by 2020. By providing AI and robotic-driven
solutions, the private sector can redefine the way healthcare is
Recently developed AI-enabled home care solutions can remotely assess patient symptoms and
alert clinicians when patient care is needed. Such home solutions can reduce needless hospital visits,
thus reducing the burden on practitioners and associated healthcare costs. For example, Accenture
estimates that such solutions can save 20% of Registered Nurse (RN) time. With the ability to learn
consistently, virtual assistants will eventually be capable of patient diagnosis, and will grow into
, a healthcare startup, is using AI to bring a physician to the patient’s
home. has developed an advanced medical data model capable of
linking probabilities between patient symptoms and conditions. Its innovative
Chatbot deploys machine-learning algorithms and NLP to communicate with
patients directly and deliver health advice based on symptoms. A substantial
medical vocabulary library helps identify the patient’s symptoms. The
Chatbot personalizes questions based on patient variables such as age and gender, to offer suggestions or connect the
patient to a physician where necessary. ML-enabled, Chatbot can learn and improve its informational diagnosis capabilities
from each patient
LifeGraph, another cutting-edge startup in the AI space, is making an
impact on the health and lives of individuals every day. LifeGraph has
developed a clinically proven, smart-phone based behavioral monitoring
solution servicing patients suffering from behavioral illnesses. LifeGraph leverages built-in smart-phone sensors to track
patients’ sleep, motion, and vocal attributes. Through machine-learning algorithms, LifeGraph is capable of detecting real-
time changes in a patient’s clinical behavior two to four weeks prior to the occurrence of a mental health episode. LifeGraph’s
continuous monitoring abilities have the potential to prevent hospitalization and significantly improve patient medication
How does it work? A patient can download the application and allow the application to operate passively on the smart-
phone device, without external interference. If abnormalities are detected in the patient’s day-to-day behavior, which could
be identified by a change in the pitch of a patient’s voice or the patient’s travel patterns, an alert log is displayed through a
dashboard, delivering information and warning signs. ML algorithms adapt to the patient and send necessary alerts to the
doctor when a patient exhibits out of the ordinary behavior. The application provides objective information, previously
revealed only after deterioration and hospitalization have
AI-enabled technologies with ML capabilities, such as LifeGraph, hold significant potential to improve health and provide
accurate information to practitioners. In addition, such tools have potential to substantially increase patient compliance with
necessary treatments, reduce hospitalization rates and, ultimately, enhance the quality of a patient lives.
40 Capital Group “The Long View: Investment Insights” (2017)
41 Accenture, “Artificial Intellignece: Healthcare’s New Nervous System” (2017)
42 Digital Trends, “The Chatbot Will See You Now: AI May Play Doctor in the Future of Healthcare” (2016)
43 CNBC, “How your smartphone could predict and prevent your next nervous breakdown” (2016)
16
AI and ML Healthcare Applications Industry Landscape
The AI and ML Healthcare market is highly fragmented and characterized by three major categories of companies: 1) diversified healthcare corporations increasingly
developing AI capabilities, 2) technology giants exploring AI applications in multiple industries and 3) AI-focused startups (See Figure 15). Large technology and
healthcare corporations are investing in niche AI startups, whose specialized expertise and concentrated talent pool drives substantial scarcity value – in 2016,
corporate participation in financing increased 3x compared to 2013. The table below depicts the major players currently competing and collaborating in the industry:
Figure 15: Representative Healthcare Artificial Intelligence Industry Landscape
17
Financing Activity & Key Partnerships
Venture Capital Activity
AI is among the most active areas for venture capital (VC) activity – 2016 gave rise to ~$5 billion in disclosed AI financing,
across a variety of industries, in over 700 Healthcare AI companies have been the leading recipients of investments
since 2012. Healthcare AI VC deal volume and funding hit a 5 year high in 2016, with $794 million in investments across 90
deals in the Healthcare AI Figure 16 shows some of the most active VC investors:
Figure 16: Active Venture Capital Investors in Healthcare AI
Corporate Investment
In addition to financial investors, large technology and healthcare corporations are increasingly turning to targeted
investments in AI startups to augment and propel internal corporate investment and development initiatives. Figure 17
depicts some of the most active corporate investors in the healthcare AI space:
Figure 17: Active Corporate Investors in Healthcare AI
Strategic Partnerships
To adapt to a rapidly changing healthcare environment, large traditional healthcare players are joining forces with some of
the largest technology companies through nontraditional partnerships and strategic alliances. Such relationships are
mutually beneficial – providing technology companies with the data points and research they need to drive continued
innovation within AI applications for healthcare, while concurrently improving efficiency and patient outcomes for healthcare
companies and institutions. Figure 18 depicts some of these key partnerships / alliances:
Figure 18: Strategic AI Partnerships – Technology & Healthcare Companies Working in Unison
44 CB Insights, “The State of Artificial Intelligence.” (2017)
(Google Ventures)
18
Healthcare AI Financing Activity
AI-focused healthcare and wellness startups are projected to
raise over $690 million from venture capital firms in 2017, a
modest decline from 2016 (See Figure 19).45 Early stage
financings continue to dominate the deal landscape – Seed
and Series A financings have accounted for over 50% of the
deal share from 2012 to 2014 and over 60% of deals in the
space since About 73% of financings since 2012 have
involved United States based AI companies, followed by the
United Kingdom (%) and Israel (%) – several
investments have also included companies based in China,
Canada and Total investment in healthcare AI reached
an all-time high in 2016; top financing rounds went to unicorn
companies Flatiron Health ($175 million in Series C) and
iCarbonX ($154 million in Series A), each valued at over one
billion The table below provides a summary of
financing activity within the healthcare AI space in the twelve-
month period ended September 2017:
45 CB Insights, “Up and Up: Healthcare AI Startups See Record Deals.” (August 22, 2017)
46 CB Insights, “The State of Artificial Intelligence.” (2017)
Figure 19: VC Funding for Healthcare AI
($ in millions)
Date
Closed Company Deal Type Investor Company Description
Investment
Amount
9/28/2017 VoxelCloud Early Stage VC Sequoia Capital; Tencent; United
Capital Investment
Provides automated medical image analysis services and
diagnosis assistance platform
$
9/27/2017 Cardinal Analytx Series A Led by Cardinal Partners Provides healthcare analytical services intended to predict
healthcare spending
$
9/26/2017 Analytics for Life, Inc. Series B Accredited Investors Develops radiation-free cardiac imaging technology for coronary
artery disease diagnosis
$
9/21/2017 Precision Health
Intelligence
Series A SymphonyAI Develops a platform designed to apply artificial intelligence into
Oncology
$
9/21/2017 Cogitativo Series A Health Care Service Corporation Provides a data science as service platform dedicated to
improving healthcare operations
$
9/19/2017 Siris Medical Series A1 California Institute for Quantitative
Biosciences & DigiTx Partners
Develops an artificial intelligence treatment decision support
system in radiation therapy
$
9/12/2017 Ieso Digital Health Later Stage VC Ananda Ventures; Draper Esprit;
Touchstone Innovations
Develops a digital mental health delivery platform designed to
transform mental health delivery
$
9/7/2017 Sophia Genetics Accelerator /
Incubator
Microsoft Accelerator Develops a clinical genomics analysis platform designed to
perform routine diagnostic testing
—
9/7/2017 WuXi NextCODE
Genomics
Series B Led by Temasek Holdings and Yunfeng
Capital
Provides a global genomics platform created to provide genomic
sequence data
$
9/5/2017 Aifloo Series A EQT Ventures Develops an e-health system designed to detect health problems $
8/21/2017 GNS Healthcare, Inc. Growth Not Disclosed Provides analytics solutions for the healthcare industry $
8/16/2017 MedAware Ltd Series A Becton, Dickinson and Company;
OurCrowd Ltd.; Gefen Capital
Develops solutions to detect and eliminate prescription errors $
8/14/2017 Freenome Inc. Series A Led by Andreessen Horowitz LLC Develops a genomic thermometer $
7/28/2017 BioAge Labs, Inc. Series A Led by Andreessen Horowitz LLC Develops a machine learning powered platform to measure human
aging and accelerate drug discovery
$
7/28/2017 Miew Growth Not Disclosed Develops artificial intelligence and software for the medical
industry
$
7/27/2017 CloudMedx Inc. Venture Not Disclosed Provides cloud-based predictive health analytics and care
coordination platform
—
7/17/2017 Synaptive Medical Inc. Growth 18 Investors Develops solutions that combine informatics, imaging, surgical
planning, navigation and advanced optics
$
Selected Healthcare Artificial Intelligence Company Financing Activity (October 2016 through September 2017)
19
($ in millions)
Date
Closed Company Deal Type Investor Company Description
Investment
Amount
7/14/2017 Imsight Medical
Technology Co. Ltd.
Series A Lenovo Capital and Incubator Group Develops artificial intelligence assisted medical imaging analysis
tools
$
7/3/2017 Mendel Health Inc. Seed DCM; LaunchCapital LLC;
BootstrapLabs
Develops a technology to architect intricate clinical trial matching
service
$
6/23/2017 Nimblr, Inc. Seed Ideas & Capital and On Ventures Develops an artificial intelligence powered software assistant that
manages healthcare appointments
$
6/14/2017 Element AI Inc. Series A Led by Data Collective Operates platform that helps organizations identify opportunities to
use AI and ML
$
6/8/2017 Multiplier Solutions Pvt.
Ltd.
Venture Norwest Venture Partners Develops a data analysis platform for the healthcare industry $
6/1/2017 Aidence . Seed Northzone Ventures; HenQ Capital
Partners; Health Innovations
Develops and delivers a platform for radiologists that detects and
classifies disorders on multiple imaging modalities
$
6/1/2017 , Inc. Venture Not Disclosed Develops virtual medical assistant application $
5/24/2017 VoxelCloud Inc. Series A Sequoia Capital Develops cloud computing and artificial intelligence solutions to
assist with interpreting medical images and clinical data
$
5/24/2017 Insight Rx, Inc. Venture GreatPoint Ventures; OSF Ventures Operates a cloud-based platform that applies quantitative
pharmacology and machine learning to improve patient care
$
5/17/2017 Oncora Medical, Inc. Venture Ben Franklin Technology Partners Develops an analytics platform that helps radiation oncologists to
use data to provide the care for cancer patients
—
5/11/2017 Guardant Health Inc. Series E Led by SoftBank Capital Provides sequencing and rare-cell diagnostics services focusing
on cancer
$
5/9/2017 Edico Genome Inc. Series B Led by Dell Technologies Capital Develops bioinformatics processor chip that helps in DNA analysis $
5/9/2017 Viz Seed Led by DHVC Operates as an AI medical imaging company that helps optimize
emergency treatment
$
4/26/2017 GRAIL, Inc. Series B Led by Arch Venture Partners Develops a blood screening test for early cancer detection $
4/25/2017 AltheaDx, Inc. Series D Not Disclosed Operates as a molecular diagnostics company for
pharmacogenetics (PGx) in the United States
$
4/25/2017 Babylon Healthcare
Services Limited
Venture Not Disclosed Operates a subscription based mobile healthcare application $
4/24/2017 Niramai Health Analytix Seed Led by Pi Ventures Develops cancer screening SaaS software that uses machine
intelligence over thermography images
—
4/24/2017 Care Design Institute
Inc.
Venture Innovation Network Corporation of
Japan
Develops and offers a nursing care platform $
4/6/2017 OM1, Inc Series A Led by General Catalyst Partners Develops a platform that enables healthcare organizations and
other stakeholders to collect and leverage health outcomes data
$
3/16/2017 Notable Labs, Inc. Venture Not Disclosed Develops and operates a platform that provides lab testing
services for brain cancer patients
$
2/22/2017 Leaf Healthcare, Inc. Growth Smith & Nephew plc Designs and develops wireless patient monitoring solutions to
improve patient safety and clinical outcomes
—
2/13/2017 SigTuple Technologies Series A Led by Accel Partners Develops solutions for the automated analysis of medical images
and data to aid diagnosis
$
2/9/2017 HealthReveal, Inc. Series A Led by GE Ventures Leverages advanced analytics and biomonitoring for the detection
and management of chronic disease
$
2/1/2017 CoheroHealth, LLC Series A Led by Three Leaf Ventures Develops medication inhaler sensors $
1/31/2017 FRONTEO Healthcare,
Inc.
Venture FRONTEO, Inc. Provides artificial intelligence-based medical data analysis
solutions
$
1/25/2017 KenSci Inc. Series A Ignition Partners; Osage Partners LLC;
Mindset Ventures
Develops a vertically integrated machine learning platform for
healthcare clients
$
1/12/2017 Nanotech Galaxy, Inc. Accelerator Techstars Central LLC Provides an artificial intelligence platform that analyzes medical
imaging to provide predictive patient insights
$
1/5/2017 PatientsLikeMe Inc. Growth The Invus Group, LLC; iCarbonx Operates a healthcare data-sharing platform $
12/31/2016 Beijing Tuixiang
Technology Co.
Venture Sequoia Capital China Develops artificial intelligence system for analyzing the CT scans
and diagnosis of thoracic pulmonary disease
—
12/15/2016 Tiny Kicks, LLC Venture Not Disclosed Develops a wireless smart sensor system that predicts and guide
healthy pregnancy outcomes
$
12/5/2016 Innoplexus AG Pre-Series A HCS Beteiligungsgesellschaft Develops and provides platforms for health care industry using
artificial intelligence
—
11/23/2016 GNS Healthcare, Inc. Growth Not Disclosed Provides analytics solutions for the healthcare industry $
11/22/2016 Recursion
Pharmaceuticals, Inc.
Series A Led by Lux Capital Management Researches, discovers and develops pharmaceuticals that focus
on a molecular target related to rare genetic diseases
$
11/16/2016 analyticsMD, Inc. Series A Co-led by Mayfield Fund and Norwest
Venture Partners
Provides software solutions that streamline hospital operations
using real time data analytics
$
10/5/2016 Welltok, Inc. Series E 27 New and Existing Investors Designs and develops a technology that assists consumers in
optimizing personal health
$
10/5/2016 DocSynk, Inc. Seed Naya Ventures Provides a big data platform that uses machine learning to
enhance patient engagement
$
Selected Healthcare Artificial Intelligence Company Financing Activity (October 2016 through September 2017)
20
Healthcare AI M&A Activity
To date, most of the investment in healthcare AI has come in the form of VC financing – reflecting the early stage of the
industry. The abundance of VC interest in healthcare AI startups is an indicator of future M&A activity, which will occur as
the industry matures and these startups continue to scale – around 48% of AI companies acquired since 2012 have had
VC Corporate tech giants, such as Google, IBM and Salesforce, who are already active in AI investing, are also
racing to acquire private AI companies, including participants in the healthcare space. Since 2012, over 250 private
companies using AI algorithms across different verticals have been Google has been the most active buyer of
healthcare AI startups – most notably purchasing deep learning and neural network startup DNNresearch in 2013 and
DeepMind Technologies in 2014 (for $600 million). Salesforce acquired AI startup MetaMind, whose deep learning
capabilities include medical image understanding, for $48 million in 2016. IBM acquired Truven Health Analytics, a leading
provider of cloud-based healthcare data, analytics and insights, for $ billion in 2016 – this is the Company’s fourth major
health data related acquisition since launching the Watson health unit in April 2015. The table below provides a summary
of M&A activity within the healthcare AI space in the twelve-month period ended September 2017:
47 CB Insights, “The Race For AI: Google, Baidu, Intel, Apple In A Rush To Grab Artificial Intelligence Startups” (July 21, 2017)
Selected Healthcare Artificial Intelligence Company M&A Activity (October 2016 through September 2017)
($ in millions)
Date
Closed Target Company Buyer Company Description
Deal
Value
Pending ZirMed, Inc. Navicure, Inc. Provides cloud-based claims management solutions to healthcare
providers and organizations
$
9/12/2017 inviCRO, LLC Konica Minolta, Inc. Provides a suite of services and software for medical imaging
analysis
$
8/7/2017 Accordion Health, Inc. Evolent Health, Inc. Develops healthcare predictive analytics software solutions $
8/3/2017 Curoverse, Inc. Veritas Genetics International Ltd. Develops and operates a cloud based open source platform for
analyzing and sharing genomic data
—
8/1/2017 CytoBioScience, Inc. Skyline Medical Inc. Manufactures devices that allow companies to understand how
human cells react to medicine
—
6/23/2017 Praxify Technologies,
Inc.
athenahealth, Inc. Develops electronic health record (EHR) applications for
physicians ad patients
$
5/13/2017 Lattice Data, Inc. Apple Inc. Offers data analytics solutions for a range of industries, including
healthcare
$
5/11/2017 DeepRadiology Inc. Senetas Corporation Limited Develops a medical machine learning software system for medical
image interpretation
—
4/24/2017 Forecast Health, Inc. Lumeris, Inc. Provides predictive analytics and planning tools to enable
population health programs for health systems
—
4/18/2017 Eliza Corporation HMS Holdings Corp. Provides health engagement management solutions $
4/4/2017 WPC Healthcare, Inc. Intermedix Corporation Develops, implements and supports data solutions for the food,
health and medicine industry
—
2/21/2017 Predixion Healthcare Jvion, LLC Offers an appliance that enables patient-level interventions to
predict adverse health events
—
1/23/2017 Meta Chan Zuckerberg Initiative Big-data-as-a-service company for science and IP —
12/22/2016 KingFit Preventive
Health & Performance
e2e Accelerator Operates a health and wellness platform powered by artificial
intelligence
—
10/19/2016 Health Data
Intelligence, LLC
TrendShift, LLC Provides cloud-based healthcare analytics solutions and evidence-
based business decision support
—
21
Conclusion: The Future of AI & ML in Healthcare
AI and ML-powered solutions have already shown the ability to perform tasks, in many cases,
better than humans. Leaders like Amazon are calling AI a “golden age” and are setting a new
standard for competitive differentiation. Amazon is pinning its future on AI as Jeff Bezos, CEO,
hinted that AI is being used in “literally hundreds of things” across the firm, stating “we are now solving problems with ML
and AI that were in the realm of science fiction for the last several decades…there is no institution in the world that can’t be
improved by machine learning.”48
Most organizations anticipate sizable benefits from AI in areas including IT, operations and manufacturing, supply chain
management, and other customer-facing activities. However, the gap between ambition and execution for AI is large at
many companies. According to research conducted by MIT Sloan Management Review, only about one in five companies
have incorporated AI in some offerings or process, revealing large gaps between industry leaders and While
nearly 85% of executives report confidence that AI will allow their companies to obtain a meaningful competitive advantage,
lack of specialized talent, establishing priorities for AI investments, and fear of job replacement are some of the key hurdles
facing AI integration.
While intelligent automation can make sense out of masses of data, people are still the most important part of the equation.
In the healthcare sector, as Humana president and CEO Bruce Broussard recently wrote, “No technology will be a substitute
for a smiling face, assisting in feeding or comforting with an arm to hold. Physicians, clinicians or family members simply
cannot be replaced by ML in health care.”50
However, the healthcare sector is particularly well positioned to benefit from the revolutionary capabilities that AI and ML
will bring, with dramatic benefits including superior patient care, reduced system-wide costs, and the opportunity for
researchers and doctors to leverage previously unimagined capabilities.
48 CNBC, “Amazon CEO Jeff Bezoz Says AI is in ‘Golden Age’” (May 8, 2017)
49 Boston Consulting Group, “Reshaping Business with Artificial Intelligence” (2017)
50 Humana News, “Why Self-Driving Cars Won’t Work in Health Care” (May 5, 2016)
22
TM Capital’s Healthcare Leadership Team
TM Capital Corp. Disclaimer
The information and opinions in this report were prepared by TM Capital Corp. (“TM”). The information herein is believed by TM to be reliable but TM makes no
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in any jurisdiction.
Michael S. Goldman
Managing Director
(212) 809-1419
mgoldman@
James I. McLaren
Managing Director
(212) 809-1414
jmclaren@
Paul R. Smolevitz
Managing Director
(212) 809-1416
psmolevitz@
23
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30th Floor
New York, NY 10022
Tel: (212) 809-1360
Boston
201 Washington Street
32nd Floor
Boston, MA 02108
Tel: (617) 259-2200
Atlanta
3575 Piedmont Road NE
Suite 1010, Building 15
Atlanta, GA 30305
Tel: (404) 995-6230 Member: FINRA/SIPC
TM Capital Corp. is a partner-owned investment banking firm based in New York, Boston and Atlanta, which has
completed over 300 transactions with a combined value in excess of $20 billion. Since 1989, we have advised clients
navigating a full range of critical transactions, including complex mergers, acquisitions, debt and equity financings,
minority and majority recapitalizations, restructurings, and advisory services including takeover defense, fairness
and solvency opinions and valuations. We have built deep industry expertise in key sectors and our team regularly
publishes research highlighting current and emerging trends in targeted industries and markets. TM Capital is a
member firm of Oaklins, the world’s most experienced mid-market M&A advisor with over 700 M&A professionals in
over 60 offices operating in the major financial centers in the world. Members have closed over 1,500 transactions
totaling more than $75 billion in value over the past five years. TM Capital is an independent investment banking
firm that is affiliated with Oaklins. Oaklins is the collective trade name of independent member firms affiliated with
Oaklins International Inc. For details regarding the nature of this affiliation, please refer to
New York
641 Lexington Avenue
30th Floor
New York, NY 10022
Tel: (212) 809-1360
Boston
201 Washington Street
32nd Floor
Boston, MA 02108
Tel: (617) 259-2200
Atlanta
1230 Peachtree Street NE
Suite 550
Atlanta, GA 30309
Tel: (404) 995-6230
