AI's balance of power: AI needs people as much as it needs data
Dina Katabi, principal investigator at the MIT Jameel Clinic, develops radio wave sensing and AI-assisted devices for passive monitoring of patients who are at high risk for Parkinson's and Alzheimer's diseases. Dina encourages greater adopting of passive health monitoring to identify population-level health trends. While she understands some are wary of the implications of AI in health and passive monitoring, Dina is confident that the technology can be managed and developed effectively through careful considerations around training algorithms and understanding limitations.
"Every machine operates properly under certain conditions and then creates bad, unreliable answers under other conditions. AI is nothing special in that sense. If you take a freezer and keep the door open, everything is going to melt," Dina says.
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.