How to create “humble” AI
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
Institute for Medical Engineering and Science (IMES)
3 Questions: Building predictive models to characterize tumor progression
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.
Using synthetic biology and AI to address global antimicrobial resistance threat
Driven by overuse and misuse of antibiotics, drug-resistant infections are on the rise, while development of new antibacterial tools has slowed.
AI algorithm enables tracking of vital white matter pathways
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.
3 Questions: Using AI to accelerate the discovery and design of therapeutic drugs
Professor James Collins discusses how collaboration has been central to his research into combining computational predictions with new experimental platforms.
Why it’s critical to move beyond overly aggregated machine-learning metrics
New research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.
MIT scientists investigate memorization risk in the age of clinical AI
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
Researchers discover a shortcoming that makes LLMs less reliable
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
Charting the future of AI, from safer answers to faster thinking
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
Using generative AI, researchers design compounds that can kill drug-resistant bacteria
The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.