Only 4 percent of all cancer therapeutic drugs under development earn final approval by the U.S. Food and Drug Administration (FDA). "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, Ph.D., professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." In a paper published October 20, 2020 in Cancer Cell, Ideker and Brent Kuenzi, Ph.D., and Jisoo Park, Ph.D., postdoctoral researchers in his lab, describe DrugCell, a new artificial intelligence (AI) system they created that not only matches tumors to the best drug combinations, but does so in a way that makes sense to humans. "Most AI systems are'black boxes'--they can be very predictive, but we don't actually know all that much about how they work," said Ideker, who is also co-director of the Cancer Cell Map Initiative and the National Resource for Network Biology.

Cardio-focused digital health company AliveCor landed FDA clearance for its new suite of interpretive ECG algorithms, dubbed the Kardia AI V2. This news comes just days after the company announced a $65 million Series E funding round. The new clearance will is able to capture sinus rhythm with premature ventricular contractions, sinus rhythm with supraventricular ectopy and a sinus rhythm with wide QRS. The algorithm works on AliveCor's KardiaMobile and KardiaMobile 6L devices, which even before this latest FDA clearance, have been able to take 30-second ECGs, and are hooked up to a corresponding app. According to the company's release, the algorithm will also reduce the number of unclassified readings, and has improved sensitivity and specificity on the company's normal and atrial fibrillation algorithms. Users will also have new visualization tools that let them see heart beat average, PVC identification and tachogram.

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

See also the article by Tadavarthi et al in this issue. Evis Sala, MD, PhD, is the professor of oncological imaging at the University of Cambridge, UK and co-leads the Advanced Cancer Imaging Programme and the Integrative Cancer Medicine Programme for the Cancer Research UK Cambridge Centre. Her current research focuses on radiogenomics through multiomics data integration for evaluation of spatial and temporal tumor heterogeneity and on the applications of AI methods for image reconstruction, segmentation, and data integration. Stephan Ursprung, MD, is a 3rd-year PhD student in the department of radiology at the University of Cambridge, UK. His research focuses on the development of AI models for automated segmentation, lesion classification, and treatment response prediction in renal cancer. Dr Ursprung's interests include health information technology, molecular and physiologic imaging, as well as multiomics data integration.

A "chemputer" is a robotic method of producing drug molecules that uses downloadable blueprints to synthesize organic chemicals via programming. Originated in the University of Glasgow lab of chemist Lee Cronin, the method has produced several blueprints available on the GitHub software repository, including blueprints for Remdesivir, the FDA-approved drug for antiviral treatment of COVID-19. Cronin, who designed the "bird's nest" of tubing, pumps, and flasks that make up the chemputer, spent years thinking of a way researchers could distribute and produce molecules as easily as they email and print PDFs, according to a recent account from CNBC. "If we have a standard way of discovering molecules, making molecules, and then manufacturing them, suddenly nothing goes out of print," Cronin stated. Beyond creating the chemputer, Cronin's team recently took a second major step towards digitizing chemistry with an accessible way to program the machine.

Some artificial intelligence breakthroughs happen in computer science labs or tense televised board games between a person and a machine. The latest advance in medical AI has less glamorous origins: the depths of US government bureaucracy. The US Centers for Medicare & Medicaid Services (CMS) recently said it would pay for use of two AI systems: one that can diagnose a complication of diabetes that causes blindness, and another that alerts a specialist when a brain scan suggests a patient has suffered a stroke. The decisions are notable for more than just Medicare and Medicaid patients--they could help drive much wider use of AI in health care. Both products are already cleared by the Food and Drug Administration and are in use by some providers. But new devices and treatments generally aren't widely used until the US government authorizes payments for Medicare and Medicaid patients.

An app designed for Apple Watch has received approval from the Food and Drug Administration (FDA) for an effective treatment for nightmares caused by post-traumatic stress disorder (PTSD). Called NightWare, the application is now marketed as an aid for the'temporary reduction of sleep disturbances related to nightmares in adults.' The app uses Apple Watch sensors to monitor body movement and sleep and when it detects the user is experiencing a nightmare, the device will vibrate to disturb their sleep. NightWare is currently only available with a prescription and the company stresses it is not a standalone treatment for PTSD. Approximately eight million Americans suffer from PTSD and up to 96 percent of them have nightmares as a result.

The lack of proper data training for AI algorithms used for medical devices can end up being harmful to patients, experts told the FDA. The federal agency held a nearly seven-hour patient engagement meeting on the use of artificial intelligence in healthcare Oct. 22, in which experts addressed the public's questions about machine learning in medical devices. Experts and executives in the fields of medicine, regulations, technology and public health discussed the composition of the datasets that train AI-based medical devices. A lack of transparency surrounding the datasets that train algorithms can lead to public mistrust in AI-powered medical tools, as these devices may not have been trained using patient data that accurately represents the individuals they will be treating. During the meeting, Center for Devices and Radiological Health Director Jeffrey Shuren, MD, noted that 562 AI-powered medical devices have received FDA emergency use authorization and pointed out that all patients should be considered when these devices are being developed and regulated.

Autonomous AI systems are rapidly making their way into the health care system, presenting regulators with thorny questions about how to protect data, prevent bias, and make sure constantly evolving machines can operate safely in clinical practice. The urgency of those inquiries will be on display Thursday during a key meeting hosted by the Food and Drug Administration, which is convening patients to collect their perspectives on AI development and regulation. The gathering of the Patient Engagement Advisory Committee comes as the agency considers crossing a crucial threshold: the approval of the first adaptive AI product, in which a system's performance changes based on its use in the real world. To date, the FDA has only approved locked systems that produce the same result based on the same input. Unlock this article by subscribing to STAT and enjoy your first 30 days free!

The U.S. Food and Drug Administration is alerting clinical laboratory staff and health care providers that false positive results can occur with antigen tests for the virus that causes COVID-19. In a letter to stakeholders, the FDA said Tuesday that while antigen tests can be used for the rapid detection of SARS-CoV-2, false positive results can occur, especially if users don't follow the instructions. "The FDA is aware of reports of false positive results associated with antigen tests used in nursing homes and other settings and continues to monitor and evaluate these reports and other available information about device safety and performance," the letter said. A Boston-area infectious disease expert said the antigen tests are good for large scale screening, when used properly, but must be followed up with more accurate testing. "If you are testing a population at low risk, it's fine to do these tests for screening," said Dr. Daniel Kuritzkes, chief of the Division of Infectious Diseases at Brigham and Women's Hospital.