AI’s balance of power

Dina Katabi, principal investigator for AI and health at the MIT Abdul Latif Jameel Clinic for Machine Leaning in Health (MIT Jameel Clinic) advocates for greater adoption of AI-assisted medical technology for population-level data generation of dynamic diseases. Her work has resulted in the development of wireless radio wave-transmitting devices that can detect subtle changes in patients' health indicators and inform disease onset and progression.

Dina considers public concern around the ethics of AI, algorithmic bias and data generation, but insists that it is up to humans to develop and train the technology correctly to avoid harmful societal outcomes, explaining, “Every machine operates properly under certain conditions and then creates bad, unreliable answers under other conditions,” she says. “AI is nothing special in that sense. If you take a freezer and keep the door open, everything is going to melt.”


An inconspicuous box sits beside the Wi-Fi router, silently humming its own much-lower-energy radio waves through the house. The patient—who has a family history of Parkinson’s disease—makes dinner, watches TV, and falls asleep. Nothing amiss.

The radio waves bounce back. Far away in the cloud, an AI-powered software program sifts through the subtle physical details that circumscribe such an ordinary day: respiration rate, blood flow, eye twitching and—there it is. After building slowly over the last month, a slight change in gait has tipped into statistically significant territory. Well before the patient feels symptoms or their doctor could notice them, the onset of the disease is documented.

Doctor and patient wake to a notification. With the alert of onset giving them valuable time, the pair meets to form a plan to curb the disease’s progression.

Hopkins Bloomberg Public Health