Dermatologists and general practitioners are somewhat less accurate in diagnosing disease in darker skin, a new study finds. Used correctly, AI may be able to help.
Although artificial intelligence in health has shown great promise, pressure is mounting for regulators around the world to act, as AI tools demonstrate potentially harmful outcomes.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
The MIT and Accenture Convergence Initiative for Industry and Technology selects three new research projects to support.
The challenge involves than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
Predictions from the OncoNPC model could enable doctors to choose targeted treatments for difficult-to-treat tumors.
MIT researchers develop “FrameDiff,” a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
