Gamifying medical data labeling to advance AI


Article by Zach Winn: “…Duhaime began exploring ways to leverage collective intelligence to improve medical diagnoses. In one experiment, he trained groups of lay people and medical school students that he describes as “semiexperts” to classify skin conditions, finding that by combining the opinions of the highest performers he could outperform professional dermatologists. He also found that by combining algorithms trained to detect skin cancer with the opinions of experts, he could outperform either method on its own….The DiagnosUs app, which Duhaime developed with Centaur co-founders Zach Rausnitz and Tom Gellatly, is designed to help users test and improve their skills. Duhaime says about half of users are medical school students and the other half are mostly doctors, nurses, and other medical professionals…

The approach stands in sharp contrast to traditional data labeling and AI content moderation, which are typically outsourced to low-resource countries.

Centaur’s approach produces accurate results, too. In a paper with researchers from Brigham and Women’s Hospital, Massachusetts General Hospital (MGH), and Eindhoven University of Technology, Centaur showed its crowdsourced opinions labeled lung ultrasounds as reliably as experts did…

Centaur has found that the best performers come from surprising places. In 2021, to collect expert opinions on EEG patterns, researchers held a contest through the DiagnosUs app at a conference featuring about 50 epileptologists, each with more than 10 years of experience. The organizers made a custom shirt to give to the contest’s winner, who they assumed would be in attendance at the conference.

But when the results came in, a pair of medical students in Ghana, Jeffery Danquah and Andrews Gyabaah, had beaten everyone in attendance. The highest-ranked conference attendee had come in ninth…(More)”