Study reveals privacy risks in medical AI
Peer-Reviewed Publication
Updates every hour. Last Updated: 23-Oct-2026 11:17 ET (23-Oct-2026 15:17 GMT/UTC)
AI models – for example, those used for cancer detection – are trained on patients’ health data. Even the mere fact that personal data has been incorporated into a model can have negative consequences for those affected if this information falls into the wrong hands. In “Nature”, a research team now shows that, using the right methods, this sensitive information can be extracted from models far more effectively than previously thought.
AI reveals critical gaps and hidden opportunities in global climate pledges. A new Nature Communications study led by CMCC researcher Francesca Larosa shows how national climate plans often overlook key development priorities, but also uncovers strong synergies that could simultaneously advance climate action and sustainable development.
Researchers developed a simple and cost-effective way to protect environmental monitoring equipment from wildfire damage. The redesigned data cabinets will keep electronics below their failure temperature during wildfires, helping maintain critical environmental data collection.
A new study led by researchers at Harvard Pilgrim Health Care Institute, with researchers from Boston University and Boston Medical Center, shows that a telehealth navigator program can significantly improve blood pressure control among Black patients with hypertension cared for at federally qualified health centers (FQHCs).
A new study shows that if data centers move a significant portion of their energy consumption to non-peak hours, it could help lower average energy costs in the U.S. The environmental impact of these changes, in terms of the type of energy consumed, would differ by region.
Advances in foundation models (FM) have opened the gates to an advanced use of artificial intelligence for healthcare. FMs are an AI system with a great ability to perform accurately on new sets of data or tasks, instead of just memorizing the material it was trained with. Vision-language foundation models (VLFM) are particularly of interest to clinicians as a means to aid in report generation and question answering, with vision-language agents (VLAs) being representative of the next frontier for AI acting in a healthcare space.