By Susie Allen

Like many fields, medicine is still figuring out how it will make the most effective use of artificial intelligence. While robots are unlikely to replace doctors anytime soon, it’s easy to imagine a future in which the two work together.

These physician–machine partnerships hold particular promise for dermatology, a specialty in which diagnosis often comes down to recognizing the visual characteristics of a disease—something that deep-learning systems (DLSs) can be trained to do with great precision.

There’s even hope that machine learning could help address a known problem in the field: only 10 percent of images in dermatology textbooks depict patients with darker skin, meaning that physicians may be unfamiliar with the different ways diseases can present across skin tones.

New research from Matt Groh, an assistant professor of management and organizations at the Kellogg School, puts the issue of machine-aided dermatology to the test by seeing how suggestions from deep-learning systems affected physicians’ photo-based diagnoses. The research was coauthored by dermatologists Omar Badri, Roxana Daneshjou, and Arash Koochek; Caleb Harris, P. Murali Doraiswamy, and Rosalind Picard of the MIT Media Lab; and Luis R. Soenksen of the Wyss Institute for Bioinspired Engineering at Harvard.

“The question was, well, does a dermatologist plus AI assistance do better or not?” Groh explains. The researchers looked not only at overall accuracy levels, but also fairness—whether accuracy levels increased evenly across images of lighter and darker skin.