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I AI INDUSTRY SERIES Top Healthcare AI Trends To WatchII 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. Healthcare is emerging as a prominent area for AI research and applications. And nearly every area across the industry will be impacted by the technologys rise. Image recognition, for example, is revolutionizing diagnostics. Recently, Google DeepMinds neural networks matched the accuracy of medical experts in diagnosing 50 sight- threatening eye diseases. Even pharma companies are experimenting with deep learning to design new drugs. For example, Merck partnered with startup Atomwise and GlaxoSmithKline is partnering with Insilico Medicine. In the private market, healthcare AI startups have raised $4.3B across 576 deals since 2013, topping all other industries in AI deal activity. III AI in healthcare is currently geared towards improving patient outcomes, aligning the interests of various stakeholders, and reducing healthcare costs. One of the biggest hurdles for artificial intelligence in healthcare will be overcoming inertia to overhaul current processes that no longer work, and experimenting with emerging technologies. AI faces both technical and feasibility challenges that are unique to the healthcare industry. For example, theres no standard format or central repository of patient data in the United States. When patient files are faxed, emailed as unreadable PDFs, or sent as images of handwritten notes, extracting information poses a unique challenge for AI. But big tech companies like Apple have an edge here, especially in onboarding a large network of partners, including healthcare providers and EHR vendors. Generating new sources of data and putting EHR data in the hands of patients as Apple is doing with ResearchKit and CareKit promises to be revolutionary for clinical studies. In our first industry AI deep dive, we use the CB Insights database to unearth trends that are transforming the healthcare industry.IV Rise of AI-as-a-medical-device Neural nets spot atypical risk factors Apple disrupts clinical trials Big pharmas AI re-branding AI needs doctors China climbs the ranks in healthcare AI DIY diagnostics is here AIs emerging role in value-based care What therapy bots can and cant do 1 3 5 8 11 14 18 20 23 Table of ContentsV At CB Insights, we believe the most complex strategic business questions are best answered with facts. We are a machine intelligence company that synthesizes, analyzes and visualizes millions of documents to give our clients fast, fact-based insights. From Cisco to Citi to Castrol to IBM and hundreds of others, we give companies the power to make better decisions, take control of their own future, and capitalize on change.VI The CB Insights platform has the underlying data included in this report WHERE IS ALL THIS DATA FROM? CLICK HERE TO SIGN UP FOR FREEVII Beti Cung, CORPORATE STRATEGY, MICROSOFT “ We use CB Insights to find emerging trends and interesting companies that might signal a shift in technology or require us to reallocate resources.” TRUSTED BY THE WORLDS LEADING COMPANIES1 Rise of AI-as-a-medical-device The FDA is fast-tracking approvals of artificial intelligence software for clinical imaging & diagnostics. In April, the FDA approved AI software that screens patients for diabetic retinopathy without the need for a second opinion from an expert. It was given a “breakthrough device designation” to expedite the process of bringing the product to market. The software, IDx-DR, was able to correctly identify patients with “more than mild diabetic retinopathy” 87.4% of the time, and identify those who did not have it 89.5% of the time. IDx is one of the many AI software products approved by the FDA for clinical commercial applications in recent months. Viz.ai was approved to analyze CT scans and notify healthcare providers of potential strokes in patients. Post FDA-approval, Viz.ai closed a $21M Series A round from Google Ventures and Kleiner Perkins Caufield & Byers. GE Ventures-backed startup Arterys was FDA-approved last year for analyzing cardiac images with its cloud AI platform. This year, 12 the FDA cleared its liver and lung AI lesion spotting software for cancer diagnostics. Fast-track regulatory approval opens up new commercial pathways for over 70 AI imaging & diagnostics companies that have raised equity financing since 2013, accounting for a total of 119 deals. The FDA is focused on clearly defining and regulating “software-as-a-medical-device,” especially in the light of recent rapid advances in AI. It now wants to apply the “pre-cert” approach a program it piloted in January to AI. This will allow companies to make “minor changes to its devices without having to make submissions each time.” The FDA added that aspects of its regulatory framework like software validation tools will be made “sufficiently flexible” to accommodate advances in AI.3 Neural nets spot atypical risk factors Using AI, researchers are starting to study and measure atypical risk factors that were previously difficult to quantify. Analysis of retinal images and voice patterns using neural networks could potentially help identify risk of heart disease. Researchers at Google used a neural network trained on retinal images to find cardiovascular risk factors, according to a paper published in Nature this year. The research found that not only was it possible to identify risk factors such as age, gender, and smoking patterns through retinal images, it was also “quantifiable to a degree of precision not reported before.” In another study, Mayo Clinic partnered with Beyond Verbal, an Israeli startup that analyzes acoustic features in voice, to find distinct voice features in patients with coronary artery disease (CAD). The study found 2 voice features that were strongly associated with CAD when subjects were describing an emotional experience. 24 A recent study from startup Cardiogram suggests “heart rate variability changes driven by diabetes can be detected via consumer, off-the-self wearable heart rate sensors” using deep learning. One algorithmic approach showed 85% accuracy in detecting diabetes from heart rate. Another emerging application is using blood work to detect cancer. Startups like Freenome are using AI to find patterns in cell-free biomarkers circulating in the blood that could be associated with cancer. AIs ability to find patterns will continue to pave the way for new diagnostic methods and identification of previously unknown risk factors.5 Apple disrupts clinical trials Apple is building a clinical research ecosystem around the iPhone and Apple Watch. Data is at the core of AI applications, and Apple can provide medical researchers with two streams of patient health data that were not as easily accessible until now. Interoperability the ability to share health information easily across institutions and software systems is an issue in healthcare, despite efforts to digitize health records. 3 This is particularly problematic in clinical trials, where matching the right trial with the right patient is a time-consuming and challenging process for both the clinical study team and the patient. For context, there are over 18,000 clinical studies that are currently recruiting patients in the United States alone.6 Patients may occasionally get trial recommendations from their doctors if a physician is aware of an ongoing trial. Otherwise, the onus of scouring through ClinicalTrials.Gov a comprehensive federal database of past and ongoing clinical trials falls on the patient. Apple is changing how information flows in healthcare and is opening up new possibilities for AI, specifically around how clinical study researchers recruit and monitor patients. Since 2015, Apple has launched two open-source frameworks ResearchKit and CareKit to help clinical trials recruit patients and monitor their health remotely. The frameworks allow researchers and developers to create medical apps to monitor peoples daily lives. For example, researchers at Duke University developed an Autism & Beyond app that uses the iPhones front camera and facial recognition algorithms to screen children for autism. Similarly, nearly 10,000 people use the mPower app, which provides exercises like finger tapping and gait analysis to study patients with Parkinsons disease who have consented to share their data with the broader research community. Apple is also working with popular EHR vendors like Cerner and Epic to solve interoperability problems. In January 2018, Apple announced that iPhone users will now have access to all their electronic health records from participating institutions on their iPhones Health app. Called “Health Records,” the feature is an extension of what AI healthcare startup Gliimpse was working on before it was acquired by Apple in 2016.7 In an easy-to-use interface, users can find all the information they need on allergies, conditions, immunizations, lab results, medications, procedures, and vitals. In June, Apple rolled out a Health Records API for developers. Users can now choose to share their data with third-party appli- cations and medical researchers, opening up new opportunities for disease management and lifestyle monitoring. The possibilities are seemingly endless when it comes to using AI and machine learning for early diagnosis, driving decisions in drug design, enrolling the right pool of patients for studies, and remotely monitoring patients progress throughout studies. “More than 500 doctors and medical researchers have used Apples ResearchKit and CareKit software tools for clinical studies involving 3 million participants on conditions ranging from autism and Parkinsons disease to post-surgical at-home rehabilitation and physical therapy.” Apple8 Big pharmas AI re-branding With AI biotech startups emerging, traditional pharma companies are looking to AI SaaS startups for innovative solutions. In May 2018, Pfizer entered into a strategic partnership with XtalPi an AI startup backed by tech giants like Tencent and Google to predict pharmaceutical properties of small molecules and develop “computation-based rational drug design.” But Pfizer is not alone. Top pharmaceutical companies like Novartis, Sanofi, GlaxoSmithKlein, Amgen, and Merck have all announced partnerships in recent months with AI startups aiming to discover new drug candidates for a range of diseases from oncology and cardiology. 49 “The biggest opportunity where we are still in the early stage is to use deep learning and artificial intelligence to identify completely new indications, completely new medicines.” BRUNO STRIGINI, FORMER CEO OF NOVARTIS ONCOLOGY Interest in the space is driving the number of equity deals to startups: 20 as of Q218, equal to all of 2017. While biotech AI companies like Recursion Pharmaceuticals are investing in both AI and drug R&D, traditional pharma companies are partnering with AI SaaS startups. Although many of these startups are still in the early stages of funding, they already boast a roster of pharma clients. There are few measurable metrics of success in the drug formulation phase, but pharma companies are betting millions of dollars on AI algorithms to discover novel therapeutic candidates and transform the drawn-out drug discovery process.10 AI has applications beyond the discovery phase of drug development. In one of the largest M&A deals in artificial intelligence, Roche Holding acquired Flatiron Health for $1.9B in February 2018. Flatiron uses machine learning to mine patient data. Today, over 2,500 clinicians use Flatirons OncoEMR an electronic medical record software focused on oncology and over 2 million active patient records are reportedly available for research. Roche hopes to gather real world evidence (RWE) analysis of data in electronic medical records and other sources to determine the benefits and risks of drugs to support its oncology pipeline. Apart from use by the FDA to monitor post-marketing drug safety, RWE can help design better clinical trials and new treatments in the future.11 AI needs doctors AI companies need medical experts to annotate images to teach algorithms how to identify anomalies. Tech giants and governments are investing heavily in annotation and making the datasets publicly available to other researchers. Google DeepMind partnered with Moorfields Eye Hospital two years ago to explore the use of AI in detecting eye diseases. Recently, DeepMinds neural networks were able to recommend the correct referral decisions for 50 sight-threatening eye diseases with 94% accuracy. This was just the Phase 1 of the study. But in order to train the algorithms, DeepMind invested significant time into labeling and cleaning up the database of OCT (Optical Coherence Tomography) scans used for detection of eye conditions and making it “AI ready.” Clinical labeling of the 14,884 scans in the dataset involved various trained ophthalmologists and optometrists who had to review the OCT scans. Alibaba had a similar story when it decided to venture into AI for diagnostics around 2016. 512 “The samples needed to be annotated by specialists, because if a sample doesnt have any annotation we dont know if this is a healthy person or if its a sample from a sick person This was a pretty important step.” - MIN WANLI, ALIBABA CLOUD According to Min Wanli, chief machine intelligence scientist for Alibaba Cloud, once the company partnered with health institutions to access the medical imaging data, it had to hire specialists to annotate the imaging samples. AI unicorn Yitu Technology, which is branching into AI diagnostics, discussed the importance of having a medical team in an interview with the South China Morning Post. Yitu claims it has a team of 400 doctors working part time to label medical data, adding that higher salary ranges for US doctors may make this an expensive option for US AI startups. But in the US, government agencies like the National Institute of Health (NIH) are promoting AI research. The NIH released a dataset of 32,000 lesions annotated and identified in CT images anonymized from 4,400 patients in July this year. Called DeepLesion, the dataset was formed using images marked by radiologists with clinical findings. It is one the largest of its kind, according to the NIH.13 Large enough to train a deep neural network, the NIH hopes that the dataset will “enable the scientific community to create a large-scale universal lesion detector with one unified framework.” Private companies like GE and Siemens are also looking at ways to cr
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