Scientists at the University of California, Riverside, have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by the novel coronavirus, or SARS-CoV-2.

"There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research.

Engineers at the UCLA Samueli School of Engineering and their colleagues at Stanford School of Medicine have demonstrated that drug levels inside the body can be tracked in real time using a custom smartwatch that analyzes the chemicals found in sweat. This wearable technology could be incorporated into a more personalized approach to medicine - where an ideal drug and dosages can be tailored to an individual.

Biomedical engineers at Duke University have shown that different strains of the same bacterial pathogen can be distinguished by a machine learning analysis of their growth dynamics alone, which can then also accurately predict other traits such as resistance to antibiotics.

About 50% of people who take the drug infliximab for inflammatory bowel diseases, such as Crohn's disease, end up becoming resistant or unresponsive to it.

Scientists might be able to catch problems like this one earlier in the drug development process, when drugs move from testing in animals to clinical trials, with a new computational model developed by researchers from Purdue University and Massachusetts Institute of Technology.

A team of researchers led by Cunjiang Yu, Bill D. Cook Associate Professor of Mechanical Engineering at the University of Houston, has developed a new form of electronics known as "drawn-on-skin electronics," allowing multifunctional sensors and circuits to be drawn on the skin with an ink pen.

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges.

While the technology for developing artificial intelligence-powered chatbots has existed for some time, a new viewpoint piece in JAMA lays out the clinical, ethical, and legal aspects that must be considered before applying them in healthcare. And while the emergence of COVID-19 and the social distancing that accompanies it has prompted more health systems to explore and apply automated chatbots, the authors still urge caution and thoughtfulness before proceeding.