2020
Healthcare AI
Trends To Watch
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3
Healthcare AI Trends To Watch
Table of Contents
Faster, cheaper, better: The next
generation of MRI and CT scans
Instant blood and at-home rapid testing:
AI will edge out labs for certain tests
Telepathology: AI and digital slides will
be a new normal for labs
AI will bring innovation and efficiency
to early drug discovery
From nursing homes to quarantine wards,
AI-driven passive monitoring takes off
Federated learning: Hospitals, pharma
partner for better AI
Hospitals tap into AI, RPA for revenue
cycle management
6
9
11
15
17
20
23
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Healthcare AI Trends To Watch
AI in healthcare has gained significant traction during the pandemic.
The White House launched an open AI challenge with Microsoft,
Allen Institute of AI, and others to mine around 30,000 scientific
papers for insights into Covid-19. Tech incumbents like Nvidia and
Alibaba have used AI to detect Covid-induced symptoms in CT
scans. Meanwhile, assisted living facilities started experimenting
with AI-enabled passive monitoring tech to reduce healthcare
workers’ risk of exposure to the virus.
Reflecting this momentum, healthcare AI companies raised more
than $2B in Q3’20, a new record for quarterly investment in the space.
The Covid-19 pandemic has reshaped the
healthcare industry, creating new demand
for artificial intelligence (AI) as key players
look to adapt. Healthcare organizations
around the world are turning to the tech to
help meet capacity challenges, accelerate
the search for a coronavirus vaccine,
transition to telehealth, and more.
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Healthcare AI Trends To Watch

Companies are also raising large rounds as the space matures. A
total of 11 healthcare AI companies closed $100M+ rounds since
March 2020, mostly driven by interest in AI for drug R&D.
Some of the themes that have emerged during the pandemic will
have a lasting impact on the healthcare industry. In this report,
we look at 7 healthcare AI trends that have been accelerated by
Covid-19 and dig into what comes next for the space.
6
Healthcare AI Trends To Watch
AI in radiology will not only drive down costs, but reduce the time
patients spend at imaging centers and lower their exposure to
radiation and heavy metals during the process.
One of the leading drivers of deals in healthcare AI is the use of
computer vision in radiology to detect anomalies in medical scans
and aid in disease diagnosis.
With big tech companies and numerous startups entering this
space since 2014, the market is flooded with AI products for
diagnostic radiology. Many of these players quickly repurposed
their products to look for signs of Covid-19 in lung CT scans.
The impact of AI-assisted diagnosis on healthcare costs will
become more pronounced in the coming months.
AI company Ezra, for example, wants to replace expensive and
invasive prostate biopsy procedures for cancer detection in men
with cheaper MRI options. Ezra claims its recently FDA-cleared AI
software improves diagnostic accuracy over traditional prostate
biopsies, while also making the procedure cost-competitive by
introducing automation into radiologists’ workflows.
Faster, cheaper, better:
The next generation of MRI and CT scans
7
Healthcare AI Trends To Watch
Source: Ezra
The average cost for a prostate biopsy is more than $2,000,
according to research published in the Journal of Urology. Ezra is
offering prostate MRIs directly to consumers for $575.
Apart from using AI, Ezra relies on a partner network to further cut
costs for patients. It has partnered with RadNet — an outpatient
MRI imaging service network with 290+ centers in the US — to
book MRI slots in bulk.
The next wave of radiology AI applications are moving beyond
disease diagnosis to image enhancement — the process by which
the radiology scans are obtained in the first place.
AI algorithms are able to generate high-resolution CT or MRI
scans with much less data than is required for conventional
approaches. This means patients can be exposed to lower levels
of radiation (in the case of X-ray/CT scans) or heavy metals like
gadolinium (MRIs).
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Healthcare AI Trends To Watch
Facebook’s joint research with NYU Langone Health, called
fastMRI, uses AI to construct high-quality MRI images using
a quarter of the data required by traditional methods, with the
potential to reduce MRI scanning times from 1 hour to 15 minutes.
The tech may also help hospitals deal with a spike in demand for
scans after the pandemic, according to Blackford, a marketplace
that connects radiologists to medical imaging software. Blackford
recently started offering AI software developed by Subtle Medical.
Source: Subtle Medical
Subtle is working on AI image enhancement for faster and higher-
quality PET and MRI scans. In August 2020, it received a $1.6M
grant from the National Institute of Health (NIH) to develop new AI
software that could help reduce the needed dosages of gadolinium
— a heavy metal that can help assess tumors — given to patients
during MRI scans. Gadolinium can reportedly have long-term toxic
effects in the body and can reduce access to MRIs for patients
with conditions like chronic kidney disease.
AI-enabled image enhancement, bundled with AI-assisted
diagnostics, has the potential to drastically reduce the associated
costs of radiology scans while improving safety and accessibility.
9
Healthcare AI Trends To Watch
Computer vision is turning smartphones into powerful diagnostic
tools and reducing the need for expert interpretation of some
test results.
Gauss Surgical, an AI company that hit the market with a blood
loss monitoring platform for operating rooms, expanded its tech to
consumer diagnostics during Covid-19.
Gauss partnered with biotech company Cellex to develop at-
home Covid-19 rapid diagnostic kits. To conduct its antigen test,
consumers are guided to apply a nasal swab using one of Cellex’s
at-home test kits. Gauss’ AI app then prompts users to scan the
test with their smartphones — neural networks process the image
and display a result within seconds.
Source: Gauss
Gauss claims that the new Gauss-Cellex at-home antigen test,
with its AI layer, “enables non-expert users to perform and
interpret the test with an iPhone or Android phone.” The antigen
test is pending FDA approval.
Instant blood and at-home rapid testing:
AI will edge out labs for certain tests
10
Healthcare AI Trends To Watch
While a type of molecular testing called polymerase chain reaction
(PCR) is considered more accurate for Covid-19 testing, the
results can take up to a week to be delivered in some cases. Due
to the demand for quick turnarounds amid the pandemic, the FDA
has given emergency approval to companies like Cellex, which
developed antibody tests that can be performed in as little as 15
minutes at labs.
Now companies like Gauss are leveraging computer vision to
speed up diagnostics even more and pair them with patients’
smartphones.
In this vein, Healthy.io set out to make urine analysis “as easy
as taking a selfie.” Its first product, Dip.io, uses the traditional
urinalysis dipstick to monitor a range of urinary infections.
Computer vision algorithms then analyze the test strips using a
smartphone’s camera.
Healthy.io has since expanded its applications to prenatal testing
and at-home chronic kidney disease testing.
Beyond consumer tests, computer vision is enabling instant point-
of-care diagnostics. For example, providers can use the tech to
conduct some types of blood tests without the need for a third-
party laboratory.
Sight Diagnostics, which raised $71M in fresh funding amid the
pandemic, has developed a complete blood count (CBC) analyzer
that can return results within minutes. The tech is awaiting FDA
approval for point-of-care use in the US.
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Healthcare AI Trends To Watch
A skills shortage coupled with social distancing measures is
accelerating the adoption of digital pathology and AI.
Although not as rapid as the proliferation of AI in radiology,
pathology AI has been gradually gaining traction — a pace that has
quickened as more labs adopt digital technologies in response to
the Covid-19 pandemic.
In traditional workflows, after a patient goes in for a lab test, the
tissue or other biological sample is treated with a stain and sent to
a pathologist, who then analyzes the sample under a microscope.
If the pathologist is unable to form a conclusive diagnosis for a
disease, the sample is packaged and shipped to another location
for a second opinion.
The excerpt below from a Google AI blog post highlights the
complexity involved in analyzing pathology slides and the chances
of misdiagnosis.
“The reviewing of pathology slides is a very complex
task, requiring years of training to gain the expertise
and experience to do well. Even with this extensive
training, there can be substantial variability in the
diagnoses given by different pathologists for the
same patient, which can lead to misdiagnoses. For
example, agreement in diagnosis for some forms of
breast cancer can be as low as 48%, and similarly
low for prostate cancer.”

— MARTIN STUMPE AND LILY PENG, GOOGLE AI
Telepathology: AI and digital slides
will be a new normal for labs
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Healthcare AI Trends To Watch
In digital pathology, an imaging device is used to take high-
resolution images of the stained sample. Instead of analyzing
slides under a microscope, a pathologist can remotely view the
images on a computer, collaborate with other medical experts via
cloud-based software, and leverage AI to help with image analysis
and diagnosis.
In 2017, Google released research on using deep learning for
detecting tumors from microscopic samples.

Source: Google
The same year, the FDA approved Philips’ IntelliSite Pathology
Solution — the first whole-slide imaging system used to capture
and store high-resolution images of tissue samples — which
renewed broader interest in digital pathology.
LabCorp, one of the largest lab networks in the US, said in an
earnings call last year that its pathology AI bets were long-term
investments with no “material near-term impact.” But the current
healthcare crisis and remote working requirements have catalyzed
this trend.
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Healthcare AI Trends To Watch
“...enabling this kind of a remote workforce
hasn’t been as much of an issue [for labs]...
but it’s because of COVID and the demand and
stress that it’s placed on these labs that we’re
looking much more aggressively at remote
collaborative workflows.”

— NATHAN BUCHBINDER, PROSCIA CPO
New York-based cancer detection company Paige AI, which raised
a $5M round from Goldman Sachs in April 2020, drew another
follow-on round of $20M in July, citing increased demand for its
products amid the pandemic as a contributing factor.
The moves toward digital pathology are global.
In the UK, pathology AI company Ibex Medical Analytics partnered
with pathology provider London Digital Pathology (LDPath). The
CEO of LDPath said in a press release that the partnership will
help the company “handle the anticipated surge in the volume
of tests and an increase of the pathology workload once we
emerge from this pandemic.” The UK is already facing a shortage
of pathologists, which may lead to a delay of several weeks for
diagnosing cancer in individual cases.
In September 2020, Puerto Rico-based CorePlus, a lab that
specializes in prostate cancer diagnosis, announced that it has
“moved away from microscopy-based pathology to AI-powered
digital pathology” through a partnership with Ibex. Meanwhile,
Philips recently partnered with the Singapore General Hospital
(SGH) to digitize SGH’s pathology workflow, with the aim of saving
about 12,000 hours each year.
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Healthcare AI Trends To Watch
A shortage of pathologists is a recurring theme around the world,
and many are anticipating a surge in demand for lab tests after the
pandemic. Stakeholders that were not incentivized to digitize their
operations before may now be facing pressure to leverage AI and
imaging technology to transition to telepathology.
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Healthcare AI Trends To Watch
From understanding viral structures to homing in on promising
compounds, AI can cut down pre-discovery times for new drugs
from years to months.
Bringing a new drug to market can take a decade or more from
initial research to distribution. But with governments scrambling
for a vaccine for Covid-19, companies are looking at a multifold
acceleration of this timeline.
Since the beginning of the pandemic, startups, universities, and big
pharma have used AI to better understand the structure of the novel
coronavirus, identify promising new compounds for treatment, find
existing FDA-approved compounds that can be repurposed, and
even design drug molecules that are structurally stable.
To study the structure of SARS-CoV-2, the virus that causes
Covid-19, researchers at the University of Texas at Austin and the
National Institute of Health (NIH) used software called cryoSPARC
to create a 3D model of the virus from 2D images captured
using cryo-electron microscopy — a technique that can capture
molecular structures.
The cryoSPARC software, developed by Structura Biotechnology,
uses neural networks to tackle the problem of “particle picking,” or
detecting and isolating protein structures in the microscopic images.
Google is also applying AI to drug discovery. Last year, its DeepMind
subsidiary developed an algorithm, AlphaFold, to help understand
protein folding — one of the most complex challenges in genomics
— to better determine the 3D structure of proteins. During the
pandemic, DeepMind used AlphaFold to predict protein structures
associated with Covid-19 and publicly released this data.
AI will bring innovation and
efficiency to early drug discovery
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Healthcare AI Trends To Watch
Recursion Pharma has also released massive SARS-CoV-2-
related datasets publicly. In September 2020, the AI-powered
biotechnology company raised a $239M Series D round with
participation from Leaps by Bayer, Lux Capital, Data Collective,
and others.
Recursion has looked to use AI to better understand the virus. In a
controlled environment, healthy cells were infected with the SARS-
CoV-2 virus, and the microscopic images were analyzed using
deep learning to identify the physical changes that occur in these
cells as a result of the infection.
Meanwhile, Atomwise, an AI platform for small molecule drug R&D,
partnered with Columbia University, Jazan University in Saudi
Arabia, Dana-Farber Cancer Institute, and others to develop broad
spectrum therapies for coronaviruses. Cyclica, an AI-supported
drug discovery company, set up a joint venture with biotech
company Mannin Research to discover small molecule drugs for
infectious diseases, including Covid-19. Iktos, a France-based
company, partnered with research firm SRI International to use
Iktos’ AI platform to design novel molecules for therapies against
influenza, SARS-CoV-2, and other viral diseases.
AI could help speed up drug discovery in the future. While progress
in identifying drugs to combat the pandemic — of which there are
40+ vaccine candidates in human trials, according to the WHO
— cannot be directly attributed to AI, advanced computational
modeling is becoming an increasingly indispensable part of the
drug development process.
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Healthcare AI Trends To Watch
Contactless, passive biometrics is reducing healthcare workers’
risk of exposure to the virus. The tech has potential to become
mainstream beyond the current health crisis.
The advantage of passive monitoring, as opposed to data
collected from wearables, is that it doesn’t require patients or
seniors to actively wear a device all of the time.
Used in a hospital setting, the tech limits healthcare workers’
contact with Covid-19 patients, and thereby their risk of exposure
to the virus, by automating data collection on vital signs.

Source: MIT
A research team at MIT developed a device called Emerald that can
be installed in hospital rooms. Emerald emits signals which are
then analyzed using machine learning as they are reflected back.
The device differentiates between patients in a room by their
movement patterns, can sense people through some walls, and is
sensitive enough to capture subtle movements such as the rise
and fall of a patient’s chest to analyze breathing patterns.
From nursing homes to quarantine wards,
AI-driven passive monitoring takes off
18
Healthcare AI Trends To Watch
The tech is already being used by Heritage Assisted Living in
Boston to monitor Covid-19 patients.
Israel-based EarlySense develops sensors that can be attached
beneath hospital mattresses or chairs. Data and alerts are sent to
hallway monitors or handheld devices.
A graphic from an EarlySense patent. Source: USPTO
Israel’s Sheba Medical Center used the tech to monitor Covid-19
symptoms among Princess Cruise passengers quarantined in
isolation rooms. This is part of a larger push for the medical facility
to design high-tech patient rooms — an initiative accelerated by
the pandemic.
Unlike MIT’s Emerald, EarlySense sensors depend on
piezoelectricity (like patient movements on a hospital bed that
create mechanical pressure, which in turn produces electric
signals). These signals are analyzed to monitor changes in a
patient’s heart rate, respiration rate, changes in posture, and when
a patient leaves their bed.
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Healthcare AI Trends To Watch
Although the patient has to be in contact with a mattress surface or
a chair, the sensor itself does not come in contact with the patient.
Similar to Emerald, the data collection happens passively in the
background and doesn’t require a patient to actively participate.
The number of Covid-19 cases at hospitals, coupled with a
shortage of personal protective equipment, may push more
healthcare networks to invest in these and other tech-enabled
solutions to better monitor patients.
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Healthcare AI Trends To Watch
A privacy-preserving AI training approach that started with
predictive text for Android keyboards is now accelerating AI
adoption among pharma, hospitals, and others.
Federated learning, which enables increased data privacy while
still allowing companies to take advantage of AI, was initially
debuted by Google in Android keyboards to predict what a user
will type next.
The capability to protect user data while improving AI algorithms
makes federated learning a compelling option for industries
dealing with sensitive information like healthcare.
Nvidia, in particular, has been an early adopter of the tech in
healthcare.
The chipmaker introduced federated learning as part of its
hardware and software healthcare framework, called Clara,
with initial users of the tech including the American College of
Radiology, MGH & BWH Center for Clinical Data Science, and
UCLA Health.
During the Covid-19 pandemic, Nvidia partnered with Mass
General Brigham for a multinational project on AI-enabled
detection of Covid-19 from X-ray images using this approach.

Federated learning: Hospitals,
pharma partner for better AI
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Healthcare AI Trends To Watch

Source: Nvidia
The company also partnered with King’s College London to use
federated learning for brain tumor detection in 2019. Clara was
also employed to help detect tumors from mammograms in a
study undertaken by the American College of Radiology, Brazil-
based imaging center Diagnosticos da America, and others.
Nvidia is not the only big chipmaker using the tech in healthcare.
In May 2020, Intel kicked off phase 1 of its brain tumor detection
tech with Penn Medicine using federated learning to preserve
patient privacy.
Meanwhile, a number of major pharma companies — including
Amgen, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, GSK,
Institut De Recherches Servier, Janssen, Merck, and Novartis — are
building a platform called MELLODDY (Machine Learning Ledger
Orchestration for Drug Discovery).
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Healthcare AI Trends To Watch
The shared interest of reducing the cost and time it takes to bring a
new drug to market has brought these competing players together
on the project, which “aims to enhance predictive Machine
Learning models on decentralised data of 10 pharmaceutical
companies, without exposing proprietary information.”
Startups are also using the tech. China-based AI company Shukun
Technology is building AI for heart disease and stroke detection,
and is now looking to develop federated learning capabilities.
To compensate for the lack of publicly available data to train AI
algorithms, Shukun reportedly relies on over 200 hospitals and other
research institutions to access private data pertaining to 100,000
cases, each containing 200-300 medical images from patients.
“For now, Shukun has to work with each hospital
individually to obtain fresh data, and connectivity
at individual facilities is often an issue. If each
hospital could be connected to a federated data
model, only local training would be required
for them to access all the data flowing through
Shukun’s network of partners.”

— TONY KONTZER, NVIDIA BLOG
Federated learning may be well-placed to help smaller startups
bring their products to market faster and help AI applications gain
buy-in from stakeholders concerned about data privacy.
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Healthcare AI Trends To Watch
Healthcare has lagged behind other industries in implementing
robotic process automation. But demand for the tech is rising
during the pandemic.
Studies show that US hospital administrative costs exceed that of
all other countries in the world, accounting for about 25% of total
healthcare spend. Recent research has put this number at roughly
$2,500 per patient.
Hospital administrative staff deal with revenue-generating
functions like verifying a patient’s insurance eligibility, identifying
the right medical codes based on the services provided, submitting
claims to insurers, and following up with patients on outstanding
bills, among other things.
Robotic process automation (RPA), an umbrella term for
automating repetitive back-office tasks like onboarding and
document digitization, has benefited from advances in computer
vision and natural language processing. While mentions of
the tech on earnings calls hint that the initial hype might have
plateaued, it is only now that more hospitals are weighing the
benefits of using the tech for automation.
This could be attributed to the fact that the majority of RPA
vendors today are general-purpose solution providers, catering to
a wide range of industries. Few startups are specifically designing
platforms that work seamlessly with the technical and regulatory
bottlenecks unique to the healthcare sector.
Hospitals tap into AI, RPA for
revenue cycle management
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Healthcare AI Trends To Watch
Olive — a company that started as a patient check-in portal in
2012 — was early to spot the opportunity here when it pivoted to
AI-enabled RPA for hospitals. Today, the company has raised more
than $230M, with 600+ providers using Olive’s tech.
.



Going beyond the traditional software-as-a-service model, Olive
is building “AlphaSites” within hospital premises, colocating
Olive engineering and project management personnel to set up
operations at customer sites.
Alpha Health, an early-stage startup, raised $20M in June 2020
from Andreessen Horowitz and others to develop a similar revenue
cycle management service.
Another part of the revenue cycle that AI is streamlining is “charge
capture,” the process by which doctors translate patient visits and
diagnoses into medical codes that can be billed to an insurer.
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Healthcare AI Trends To Watch
Physician assistant companies like Suki and Augmedix leverage
voice tech and natural language processing to transcribe doctor-
patient interactions, automatically syncing this information with
electronic health records. These companies’ digital assistants
can also suggest medical codes corresponding to the patient visit
based on contextual information gathered from the interaction.
Many hospitals are considering cuts in IT spend to cope with
Covid-19’s impact on business. But, at the same time, demand
for services offered by companies like Olive — which raised over
$150M in total across 2 funding rounds during the pandemic —
signals that hospitals may be willing to invest to reduce longer-
term outlays and automate some aspects of financial management.
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Healthcare AI Trends To Watch
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