Q&A: A roadmap for revolutionizing health care through data-driven innovation
A new book coauthored by MIT’s Dimitris Bertsimas explores how analytics is driving decisions and outcomes in health care.
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Medicine
Making AI models more trustworthy for high-stakes settings
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
Read MoreNovel method detects microbial contamination in cell cultures
Ultraviolet light “fingerprints” on cell cultures and machine learning can provide a definitive yes/no contamination assessment within 30 minutes.
Read MoreA new method lets users ask, in plain language, for a new molecule with certain properties, and receive a detailed description of how to synthesize it.
Read MoreMIT spinout maps the body’s metabolites to uncover the hidden drivers of disease
ReviveMed uses AI to gather large-scale data on metabolites — molecules like lipids, cholesterol, and sugar — to match patients with therapeutics.
Read MoreCan deep learning transform heart failure prevention?
A deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.
Read MoreToward video generative models of the molecular world
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
Read MoreMIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
Read MoreAI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
Read MoreA vision for U.S. science success
In a talk at MIT, White House science advisor Arati Prabhakar outlined challenges in medicine, climate, and AI, while expressing resolve to tackle hard problems.
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