Top Healthcare AI Trends To Watch by CBInsights

AI needs doctors. Big pharma is taking an AI-first approach. Apple is revolutionizing clinical studies. We look at the top artificial intelligence trends reshaping healthcare.
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Healthcare AI trend company patient Tech Watch Hospital test data
AI needs doctors. Big pharma is taking an AI-first approach. Apple is revolutionizing clinical studies. We look at the top artificial intelligence trends reshaping healthcare.
About Techcelerate Ventures

Tech Investment and Growth Advisory for Series A in the UK, operating in £150k to £5m investment market, working with #SaaS #FinTech #HealthTech #MarketPlaces and #PropTech companies.
Healthcare AI trend company patient Tech Watch Hospital test data
2020
Healthcare AI  
Trends To Watch
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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.
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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.
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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
16
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.
17
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 
21
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
24
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.
25
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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