AI and the Future of cancer Treatment

Mention artificial intelligence (AI) at a social gathering and you’re likely to encounter strong feelings-both positive and negative.

For Justin Hwang, however, AI isn’t just conversation fodder. It’s his partner in a mission to lengthen and save lives.

Hwang, an assistant professor of medicine in the University of Minnesota Medical School’s division of Hematology, Oncology, and Transplantation, is deploying AI-powered algorithms to identify which cancer therapies might work best for individual people.

Hwang and his colleagues are developing and using these algorithms to plumb enormous data sets, finding meaningful patterns that can inform cancer treatment decisions. the goal is to save precious time by weeding out therapies unlikely to work for an individual and pointing care teams toward treatments that should.

The approach involves both zooming in and zooming out: zooming in to examine an individual person’s genetic profile, and zooming way out-to a population level-to find patterns among thousands, even millions, of other people with similar genetic markers.

“It’s almost like we can see how each individual fits into this massive population based on their genomics,” Hwang says. “Is there a pattern related to good outcomes? Bad ones? To drug sensitivity or insensitivity? Why are certain people more responsive [to some treatments] while others are less responsive? We’re essentially using genomics to assign a person’s trajectory.”

Making sense of the search

The exploding availability of DNA, mRNA, patient data from health records, diagnostic images, and other digital sources is key for researchers like Hwang and his colleagues.

Having that data is one thing; making sense of it is something else entirely.

With support from philanthropy, including the Randy Shaver Cancer Research and Community Fund, Hwang and his team are developing machine learning algorithms-a type of AI that allows computers to learn from data sets-to uncover meaning from previously inscrutable mounds of data.