The benefits of medical AI assistance vary based on user expertise
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
The visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become one of the most comprehensive biomedical and clinical data repositories in existence.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
Founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease.
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
Assistant Professor Matthew Jones is working to decode molecular processes on the genetic, epigenetic, and microenvironment levels to anticipate how and when tumors evolve to resist treatment.
Driven by overuse and misuse of antibiotics, drug-resistant infections are on the rise, while development of new antibacterial tools has slowed.
Opening a new window on the brainstem, a new tool reliably and finely resolves distinct nerve bundles in live diffusion MRI scans, revealing signs of injury or disease.
Professor James Collins discusses how collaboration has been central to his research into combining computational predictions with new experimental platforms.