3 Questions: How to help students recognize potential bias in their AI datasets
Courses on developing AI models for health care need to focus more on identifying and addressing bias, says Leo Anthony Celi.
Courses on developing AI models for health care need to focus more on identifying and addressing bias, says Leo Anthony Celi.
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
A new book coauthored by MIT’s Dimitris Bertsimas explores how analytics is driving decisions and outcomes in health care.
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
The framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.
A deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.