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2020 Healthcare AI Trends To Watch CB Insights helps the world’s leading companies make smarter technology decisions with data, not opinion. Our Technology Insights Platform provides companies with comprehensive data, expert insights and work management tools to drive growth and improve operations with technology. WHAT IS CB INSIGHTS? SIGN UP FOR A FREE TRIAL 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 4 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. 5 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). 8 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. 11 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 12 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. 13 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. 14 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. 15 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. 19 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. 20 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). 22 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. 23 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. 26 Healthcare AI Trends To Watch This report was created with data from CB Insights’ emerging technology insights platform, which offers clarity into emerging tech and new business strategies through tools like: • Earnings Transcripts Search Engine & Analytics to get an information edge on competitors’ and incumbents’ strategies • Patent Analytics to see where innovation is happening next • Company Mosaic Scores to evaluate startup health, based on our National Science Foundation-backed algorithm • Business Relationships to quickly see a company’s competitors, partners, and more • Market Sizing Tools to visualize market growth and spot the next big opportunity If you aren’t already a client, sign up for a free trial to learn more about our platform. 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