Many medical drugs work by modulating the body’s production of specific proteins. Recent research aimed to predict this activity, enabling researchers to identify drugs that might counteract the effects of Covid-19.

It’s commonly assumed that models pretrained to achieve high performance on ImageNet will perform better on other visual tasks after fine-tuning. But is it always true? A new study reached surprising conclusions.

The pandemic has pushed hospitals to their limits. A new machine learning system could help doctors make sure the most severe cases get timely, appropriate care.

U.S. government approval of medical AI products is on the upswing — but information about how such systems were built is largely unavailable. The U.S. Food and Drug Administration (FDA) has approved a a plethora of AI-driven medical systems.

Doctors who treat breast cancer typically use a quick, inexpensive tumor-tissue stain test to diagnose the illness and a slower, more costly one to determine treatment. A new neural network could help doctors to go straight from diagnosis to treatment.

A model designed to assess medical patients’ pain levels matched the patients’ own reports better than doctors’ estimates did — when the patients were Black.

A neural network learned to read the genes of viruses as though they were text. That could enable researchers to page ahead for potentially dangerous mutations. Researchers at MIT trained a language model to predict mutations that would enable infectious viruses to become even more virulent.

Can social media posts reveal early signs of mental illness? A new machine learning model shows promising results. Researchers developed a model that analyzes messages and images posted by Facebook users for indicators of psychological problems.

Look at the tip of a standard #2 pencil. Now, imagine performing over one trillion multiplication operations in the area of that pencil tip every second. This can be accomplished using today’s 7nm semiconductor technology.

Institutional hurdles to AI for medicine began to fall, setting the stage for widespread clinical use of deep learning in medical devices and treatments. DeepMind’s AlphaFold model determined the three-dimensional shape of a protein in just hours.

AI accelerated the search for a coronavirus vaccine, detected Covid-19 cases, and otherwise softened the pandemic’s blow. Machine learning researchers worldwide scrambled to harness the technology against the coronavirus.

Covid Moonshot, an open-source project to vet potential medicines using machine learning, is closing in on compounds that might help curb Covid-19. Four new antiviral drugs identified by the project are ready to advance to animal trials.

It’s expensive to pay doctors to label medical images, and the relative scarcity of high-quality training examples can make it hard for neural networks to learn features that make for accurate diagnoses.

Will we ever understand what goes on inside the mind of a neural network?The fear: When AI systems go wrong, no one will be able to explain the reasoning behind their decisions.

Controversy erupted over the need for transparency in research into AI for medicine. Google Health introduced a system that purportedly identified breast cancer more accurately than human radiologists.