LLMs factor in unrelated information when recommending medical treatments
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
The MIT Ethics of Computing Research Symposium showcases projects at the intersection of technology, ethics, and social responsibility.
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
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.