AI for Medicine

The world’s biggest pharmaceutical company by revenue shed light on its AI strategy.

A critical step in diagnosing illnesses is a conversation between doctor and patient to assemble a medical history, discuss approaches to managing symptoms, and so on.

A deep learning model significantly reduced deaths among critically ill hospital patients.

The latest update of DeepMind’s AlphaFold model is designed to find the structures of not just proteins but all biologically active molecules as well as interactions between them.

A neural network detected early signs of pancreatic cancer more effectively than doctors who used the usual risk-assessment criteria. Researchers at MIT and oncologists at Beth Israel Medical Center in Boston...

Biologists used neural networks to find a new class of antibiotics. Researchers at MIT and Harvard trained models to screen chemical compounds for those that kill methicillin-resistant Staphylococcus aureus (MRSA), the deadliest among bacteria that have...

A microscope enhanced with augmented reality is helping pathologists recognize cancerous tissue. The United States Department of Defense is using microscopes that use machine learning models based on research from Google to detect cancers.

The United States’ regulatory regime may not be clear or flexible enough to ensure the safety of AI-powered medical devices. Physicians and other health professionals believe that U.S. regulators have approved AI-powered medical products without proper oversight or disclosure.

Large language models may help psychiatrists resolve unanswered questions about mental illness. Researchers from University College London, Beijing Normal University, and Lisbon’s Champalimaud Centre for the Unknown used a large language model to...

A deep learning system detected breast cancer in mammograms as well as experienced radiologists, according to a landmark study.

Eric Topol is one of the world’s leading advocates for AI in medicine. Below he shares his insights into the fusion of AI and medicine and advice for machine learning engineers who want to get involved.

The wearable revolution is helping doctors figure out what’s troubling your ticker — thanks to deep learning. The problem: Arrhythmias, a range of conditions in which the heart beats too fast, too slow, or erratically, can cause heart attack or stroke.

An AI-driven alarm system helps rescue patients before infections become fatal. The problem: Machine learning can spot patterns in electronic health data indicating where a patient’s condition is headed that may be too subtle for doctors and nurses to catch.

Will deep learning discover new medicines? Startups — and big-pharma partners — are betting on it. The problem: In theory, there’s a pharmacological cure for just about any ailment. In practice, discovering those therapies takes years and billions of dollars.

Doctors are overwhelmed by clerical work. Healthcare-savvy voice assistants are picking up the slack.