MIT researchers have developed a new artificial intelligence system that uses a person’s breathing pattern to help detect Parkinson’s sisease, reports Susannah Sudborough for Boston.com. “The device emits radio signals, analyzes reflections off the surrounding environment, and monitors the person’s breathing patterns without any bodily contact,” writes Sudborough.
Researchers at MIT and other institutions have developed an artificial intelligence tool that can analyze changes in nighttime breathing to detect and track the progression of Parkinson’s disease, reports Casey Ross for STAT. “The AI was able to accurately flag Parkinson’s using one night of breathing data collected from a belt worn around the abdomen or from a passive monitoring system that tracks breathing using a low-power radio signal,” writes Ross.
A study co-authored by MIT researchers finds that algorithms based on clinical medical notes can predict the self-identified race of a patient, reports Katie Palmer for STAT. “We’re not ready for AI — no sector really is ready for AI — until they’ve figured out that the computers are learning things that they’re not supposed to learn,” says Principal Research Scientist Leo Anthony Celi.
Researchers at MIT and Harvard Medical School have created an artificial intelligence program that can accurately identify a patient’s race based off medical images, reports Tony Ho Tran for The Daily Beast. “The reason we decided to release this paper is to draw attention to the importance of evaluating, auditing, and regulating medical AI,” explains Principal Research Scientist Leo Anthony Celi.
During the AI Cures Conference, Prof. Regina Barzilay spoke with Food and Drug Administration senior staff fellow Amir Khan about how the agency intends to regulate artificial intelligence in medicine, reports Casey Ross for STAT. “’My thinking is that models should be regulated based on their functionality, and not necessarily on the input data they use,” said Barzilay.
Researchers from MIT and journalists from STAT conducted a months long investigation and found that “subtle shifts in data fed into popular health care algorithms — used to warn caregivers of impending medical crises — can cause their accuracy to plummet over time, raising the prospect AI could do more harm than good in many hospitals,” reports Casey Ross for STAT.