3 Questions: Should we label AI systems like we do prescription drugs?
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
Medicine
MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
School of Engineering welcomes new faculty
Fifteen new faculty members join six of the school’s academic departments.
From steel engineering to ovarian tumor research
Ashutosh Kumar, a materials science and engineering PhD student and MathWorks Fellow, applies his eclectic skills to studying the relationship between bacteria and cancer.
Brain surgery training from an avatar
MIT.nano Immersion Lab works with AR/VR startup to create transcontinental medical instruction.
New model identifies drugs that shouldn’t be taken together
Using a machine-learning algorithm, researchers can predict interactions that could interfere with a drug’s effectiveness.
Doctors have more difficulty diagnosing disease when looking at images of darker skin
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.
What to do about AI in health?
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.
New hope for early pancreatic cancer intervention via AI-based risk prediction
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
How an archeological approach can help leverage biased data in AI to improve medicine
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.