Analysis of artificial intelligence and machine learning in healthcare

Artificial intelligence and machine learning (AI/ML) is beginning to resonate with VC investors in the healthcare sector, recording 48 deals in 2017 raising $718 million, representing 65% and 126% growth YoY, respectively.
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AI ML healthcare data patient healthcare AI Health Computer Vision deal Personal This segment represent
<p>AI's Health Exam
Analysis of artificial intelligence and machine learning in healthcare
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CAMERON STANFILL Analyst
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Contents
Key Takeaways
1
Introduction
2
Mapping the healthcare 
AI environment
3
Large-cap outlook
4
Which segments are 
prime for near-term 
success?
5
Headwinds
8
Looking forward
9
Key Takeaways 
  Artificial intelligence and machine learning (AI/ML) is beginning to resonate 
with VC investors in the healthcare sector, recording 48 deals in 2017 raising 
$718 million, representing 65% and 126% growth YoY, respectively.
  Within healthcare, we expect AI to be used mainly to augment workers' 
skills, as opposed to displacing them. Personal AI assistants for physicians/
nurses can recall relevant medical research, compile population patient data, 
provide initial possible diagnoses, and optimize workflow. Time saved by these 
innovations represent countless hours that can be used more efficiently by 
industry professionals. 
  Deep learning algorithms that leverage image recognition are already more 
accurate than panels of specialists in detecting and diagnosing pneumonia, TB, 
certain early stage cancers, and other diseases.
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2
Analysis of AI/ML in Healthcare
Introduction 
In our initial coverage of the artificial intelligence and machine learning (AI/ML) 
vertical, we concluded that the technology has the potential to impact nearly 
every industry by automating a range of tasks. Due to this widespread potential, 
we want to provide deeper analyses into specific industries to highlight the 
rapidly growing and transformative use cases for AI/ML. The potential for AI/ML 
is beginning to resonate with venture investors particularly in healthcare, as 2017 
deal count and value grew approximately 65% and 126% YoY, respectively.
Healthcare AI deal activity by year
The healthcare market is giganticrepresenting 17.9% of US GDP in 20161
and produces a massive amount of data, positioning the industry to receive 
significant benefits from further integration of AI/ML. Highlighting some of the 
overarching inefficiency, healthcare's percentage of GDP has doubled since 1980, 
but health outcomes haven't improved at the same rate. While this technology 
can increase business efficiency and profitability, AI/ML advancements in 
healthcare also aim to improve patient outcomes and enhance the overall patient 
experience. The widespread data digitization in healthcare is the backdrop 
for all potential use cases in the space, but data structure and fragmentation 
(e.g. isolated within hospitals, insurance groups) serve as significant hurdles to 
widespread adoption. 
Source: PitchBook
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The widespread data digitization in 
healthcare is the backdrop for all 
potential use cases in the space, but 
data structure and fragmentation (e.g. 
isolated within hospitals, insurance 
groups) serve as significant hurdles to 
widespread adoption.
1 Centers for Medicare and Medicaid Services
3
Analysis of AI/ML in Healthcare
Mapping the healthcare AI environment 
Upon examining the deal flow data, we have segmented the current VC 
investment in AI healthcare market into five distinct subcategories:
1. Automation & Assistance  This segment represents 40% of deals and 
includes those companies focusing on assisting or augmenting day-to-day 
processes for medical professionals and patients.
2. Computer Vision  This segment represents 19% of deals and includes 
companies pursuing applications of computer vision of medical images to aid 
in diagnosis, preventative medicine, etc. 
3. Population Health & Predictive Analytics  This segment represents 15% 
of deals and includes companies creating platforms focusing on improving 
population health using aggregated genome, patient, and treatment data, as 
well as businesses seeking to improve the healthcare system at a more macro 
level. 
4. Personalized Medicine  This segment represents 18% of deals and includes 
companies working to create tailored healthcare analytics or devices to 
improve personal health outcomes. AIenabled personal wearables and 
sensors are included in this subcategory.
5. Drug Discovery  This segment represents 8% of deals and includes 
companies using AI/ML to research and develop novel drugs.
Automa on & 
Assistance 
Computer Vision
Drug 
Discovery
Personalized 
Healthcare
Popula on Health & 
Predic ve Analy cs
VC investment in healthcare-focused AI (#) by sector2
Source: PitchBook
2 All data in this section is for the period from 20062017
4
Analysis of AI/ML in Healthcare
Large-cap outlook
 
While we can't know exactly what is mentioned behind closed doors, AI/
ML hasn't quite broken into the public commentary from executives of public 
healthcare firms. Companies have instead opted for more broad classifiers 
like "novel technology", big data/analytics, personalization of treatment and 
commitments to "business development strategies". One explicit mention 
came from the insurer United Healthcare, which stated they were continuing to 
support "existing initiatives in AI" that could benefit both the insurance practice 
as well as their healthcare solutions subsidiary Optum. Given the nascence of the 
drug discovery space within healthcare AI/ML, the relative lack of penetration 
into the large-cap pharmaceutical market was fairly expected. 
Some technology giants and AI pioneers also have ambitions within the 
healthcare sector. Most notably, one of the earliest movers is IBM's Watson 
Health, which according to the company is beginning to scalereaching 115,000 
patients and being used in 15 hospitals. Watson's breadth of applications has 
also increased over the past year; the program has now been trained to identify 
13 types of cancer, up from four a year ago. Google's DeepMind also has a health 
initiative that has partnered with the NHS in the UK on an app called Streams 
that uses technology in mobile phones to alert healthcare staff when a patient's 
health deteriorates. As companies in the healthcare AI/ML field continue to 
grow and prove their commercial capabilities, we expect the attention from 
large corporates to increase over the next 10 years, including some notable 
acquisitions. 
IBM's Watson Health, which 
according to the company is 
beginning to scalereaching 
115,000 patients and being used in 
15 hospitals.
5
Analysis of AI/ML in Healthcare
Which segments are prime for near-term success? 
Automation & Assistance
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Source: PitchBook
The first broad application of AI/ML in healthcare in which we expect to 
see widespread adoption is the automation of menial or recurring tasks via 
personal assistance for both patients and practitioners. These will be focused 
on increasing workers' knowledge and improving day-to-day efficiency to allow 
more focus on value-additive tasks, with the ultimate goals of achieving better 
health outcomes and cost savings. With over $200 million of VC investment in 
the last two years, this is a large and growing niche of the healthcare AI space.
We believe the automation function is key because of the nature of labor in 
healthcare. AI/ML-enabled personal assistants for physicians/nurses could 
recall relevant medical research, compile patient data, provide initial possible 
diagnoses, and perform scribing duties, among others. These innovations 
should allow medical practices to reallocate countless worker hours toward 
more efficient tasks. One specific example is the automation of data entry into 
electronic health records (EHRs) via speech recognition technology.
Similar benefits can be realized for patients, as AI/ML applications can improve 
ease of access through initial contact points like chatbots. Digital preliminary 
diagnoses can also limit unnecessary clinic visits and re-admissions, decreasing 
costs for patients and freeing up capacity in the healthcare system. Once 
patients enter a medical facility, AI/ML can be incorporated into initial touch-
points for patients to assess the severity of their condition to optimize physician 
scheduling.
Another costly and time-consuming task within the healthcare industry is the 
administration of clinical trials. AI/ML are aiming to help solve frictions in the 
drug approval process, from finding eligible patients to participate in trials, to 
aggregating and interpreting results. This is the niche that Clinithink, a UK-
based startup with over $22 million in VC funding, aims to serve by working to 
transform unstructured clinical trial data into structured datasets to reduce risk 
and accelerate go-to-market time for drugs. 
VC activity in automation & assistance-focused AI companies
6
Analysis of AI/ML in Healthcare
Computer vision
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Computer vision is the second area of healthcare AI in which we've seen 
extensive growth. A major use of AI/ML technology is applying it to medical 
imaging analysis, which has the potential to deliver staggering improvements 
in speed and accuracy. With the success of many general image recognition 
algorithms created by Google, Clarafai and others, we see opportunity in this 
space as nearing to commercial realization. The healthcare computer vision 
space saw a significant influx in VC investment over the last two years, tallying 
over $100 million in 2017 alone.
AI/ML algorithms built to analyze medical imaging have already exceeded 
panels of specialists in diagnosing pneumonia, TB, certain early stage cancers. 
AI implementation in diagnosis could drastically increase the amount of patient 
data that could be processed, expediting test results while increasing accuracy. 
Computer vision also applies to other visual-based diagnoses/assessments 
like dermatology. For example, Stanford's skin cancer classification algorithm 
matched the performance of dermatologists based on pictures of skin lesions, 
while a separate group of scientists developed another neural network created 
by that identifies onychomycosis (nail fungus). 
These photo-based diagnosis applications could extend the health benefits 
beyond the clinic and serve as a preliminary point of contact via patients' mobile 
phones. While early successes of AI/ML programs for medical imaging have 
fueled significant growth in financing, this market is still relatively nascent. As 
such, we expect this space will continue to receive more funding over the near 
term, and consequentially attract more new entrants.
VC activity in computer vision-focused AI companies
Source: PitchBook
These photo-based diagnosis 
applications could extend the health 
benefits beyond the clinic and serve 
as a preliminary point of contact via 
patients' mobile phones. 
7
Analysis of AI/ML in Healthcare
Personalized healthcare
 
AI/ML in the healthcare industry will also allow the opportunity for greater 
personalization of care. Armed with more detailed data on individuals, healthcare 
can be tailored based on personal history, genetics, demographics and real-
time inputs. A key use for this is changing the way we prescribe medications. 
Currently, many treatments are given out in a standardized fashion, especially 
regarding the size of the dose. This can lead not only to less effective treatments, 
but also the potential for adverse reactionsthe cause of thousands of deaths 
every year3. Chemotherapy treatments, for example, are largely thought of as 
a one-size-fits-all option; however, personalization in this instance has huge 
potential by determining the biomarkers found in the patient's tumor and 
tailoring molecular-based therapies or the most effective chemotherapeutic 
agents depending on that patient's specifics. Siris Medical is already providing 
personalized cancer treatments that should accelerate the treatment planning 
process to streamline workflow and strengthen health outcomes. 
Reinforcement learning algorithms, such as contextual bandits, provide a 
promising solution to personalization interfaces. These programs use contextual 
and historical data to decide among the available options, then receives reward-
based feedback based on decision it has made. The algorithm then learns to 
optimize its decision to the most favorable outcomes by repeating the process 
a multitude of times. This feedback process is not perfect and the bandit may 
discard a viable action during the course of its learning, so the algorithm must 
strike a balance between exploiting what it believes is current optimal decision 
while still exploring all available actions. 
The reward function, especially when dealing with biology and health, is 
fairly abstract because there aren't clean, quantifiable data points or easily 
determinable right or wrong answers, especially not relayed in a timely fashion. 
This is where the integration of real-time data from wearables and other sensors 
begins to play a key role in proving the feasibility of many of these applications 
and, ultimately, making the analytics more accurate and valuable. For example, a 
wearable device could track blood sugar data to provide feedback to the bandit 
on the selected treatment, then determine a positive or negative reward in near 
real-time that serves to improve the program. 
Note: Contextual bandits are, in 
essence, computerizations of a 
slot machine with multiple arms 
corresponding to the number of 
available options where every decision 
returns a reward to the program. 
Before the program (i.e., bandit) 
makes its next decision, it considers 
all the rewards received previously 
in conjunction with information 
about the current context (external 
data, multi-world testing, etc.). For 
example: programmatically generating 
personalized headlines on a news site 
to optimize clicks, the bandit reviews 
past decisions/rewards, headlines 
from other sources, what the user has 
clicked on before, etc. to come to a 
decision on which option to select.
3 Shepherd, Greene & Mohorn, Phillip & Yacoub, Kristina & Williams May, Dianne. (2012). 
Adverse Drug Reaction Deaths Reported in United States Vital Statistics, 1999-2006. 
The Annals of pharmacotherapy. 46.
8
Analysis of AI/ML in Healthcare
Headwinds
 
Although deployment of AI/ML in the healthcare industry might deliver better 
health outcomes in a more efficient manner, many factors will impose limitations 
on the adoption and scalability of these technologies. The implementation of 
large-scale AI programs is likely to be mired by prevalent data fragmentation 
issues, as the healthcare sector produces large amounts of unstructured 
data that are handled differently by various institutions. As more data are 
collected (via wearables, IoT, etc.), more emphasis will be put on the collection 
and cleaning of data before it can be used to train algorithms. Additionally, 
companies that choose to operate in this space must consider the regulatory and 
security issues around healthcare data, a barrier that has kept many companies 
from entering the industry. Clean datasets and the ability to securely store and 
share this data are critical to expanding capabilities of the healthcare AI field in a 
sustainable manner. 
Humans innately like to know the reasoning behind decision making, especially 
when it concerns our health. As AI/ML programs start assisting with diagnoses, 
more questions will arise about the rationale behind recommendations. 
The "black-box problem"  as this phenomenon is known  is an important 
consideration in the AI/ML field. However, as there is already a relative lack of 
interpretability of human intuition relating to complicated medical situations, 
we believe this fear in healthcare may be slightly overhyped. When an AI agent 
makes diagnoses, it could have the benefit of drawing on the complete patient 
history, aggregation of other relevant patients, relevant studies and other sources 
to come to an evidence-based conclusion. That said, it will still be important 
to continue to have humans make the final decisions as a sanity check and for 
patient confidence, because the importance of the physician's experience and 
direct contact with the patient cannot be denied. This is particularly pertinent 
when it comes to assigning culpability for misdiagnoses.
On a related note, we find another hurdle to widespread adoption of AI/ML 
in healthcare to be the preference for human relationships. Automating initial 
contact points with both physicians or administrative workers in clinics may not 
be well received by the broader patient population. However, this should be 
mitigated partially because in general we see AI/ML technology in this industry 
as working much more to assist workers rather than replace them. 
9
Analysis of AI/ML in Healthcare
Looking forward
 
On the frontier of the AI/ML healthcare field is the possibility of new drug 
discovery by machines. The spontaneity and creativity required in novel 
discovery adds an extra layer of complexity to this use case of AI/ML. Bringing a 
new drug to market is a notoriously time-consuming journey that can take more 
than a decade and between $650 million and $2+ billion of investment4. Many 
times, large-cap pharma & biotechs try to avoid this commitment by acquiring 
smaller companies with promising drugs already in the clinical trial process. AI/
ML programs may shine in this context due to their ability to model a multitude of 
outcomes which help predict side effects or lack of efficacy, thus reducing failure 
rates and rapidly shortening R&D timelines, while potentially allowing more 
efficient in-house drug development processes. 
Though drug discovery with AI/ML is still in a nascent stage, algorithms have 
already been used to determine whether a drug molecule will bind with the 
target protein with up to 99% accuracy and are working to predict the outcomes 
of CRISPR-Cas9 treatments. These predictive modeling techniques can also 
be applied to genome data to find novel patterns and approaches to treating 
genetic diseases.  
Conclusion 
Due to the healthcare industry's size and depth of data, it's clearly a huge market 
opportunity for AI/ML. As many applications remain in the proof-of-concept 
phase or singleclinic partnerships, the speed of adoption and total market 
penetration remains uncertain. Over the next several years, we expect the more 
developed areas like assistants, medical imaging, and diagnosis to increase 
reliability and drive further use of these platform services, with the winners 
enjoying positive network effects. As the industry receives more funding to 
continue to expand research efforts, we expect the pace of breakthroughs to 
accelerate. The prospect of improved health at the population level provided 
more efficiently and lower cost is an enticing outcome that AI/ML can provide 
and cannot be ignored. 
3 Tufts Center for the Study of Drug Development
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