Will AI Steer Oncologists or Will Oncologists Steer AI?

In this snippet of The AI Oncology Revolution discussion with Matthew Matasar, MD, he described the field as being in an “embracing the dialectic” moment, holding both deep enthusiasm for AI’s transformative potential and open questions about what oncology, as a profession, stands to lose. As evidence that AI is already reshaping drug discovery, Matasar pointed to the first drug to move from an AI model detecting a novel target to AI designing a molecule against that target, which is now in a phase 3 trial for pulmonary fibrosis. Arturo Loaiza-Bonilla, MD, MSEd, FACP, countered residual anxiety about AI’s disruptive potential with the Jevons paradox, the idea that greater efficiency may generate more demand for physician expertise, not less. They both concluded in agreement that any tool genuinely capable of curing cancer would be worth being replaced by.

Loaiza-Bonilla is systemwide chief of hematology and oncology at St. Luke’s University Health Network and co-founder of Massive Bio. Matasar is chief of the Division of Blood Disorders at Rutgers Cancer Institute/Jack & Sheryl Morris Cancer Center and professor of medicine at Rutgers Robert Wood Johnson Medical School, where his clinical and research interests focus on lymphoma.

Transcript

Loaiza-Bonilla: Thinking about what other colleagues have said about AI, what have you heard them being excited about? Are there any comments you’ve picked up in the field, not just at your institution, but broadly…?

Matasar: We’re sort of in this “embracing the dialectic” moment, where we have all this real, deep-seated optimism about the potential of AI as a transformative approach, and we have all of these misgivings: What are we doing, how is it influencing and changing our profession and our career, and is there a downside we don’t yet appreciate? The potential is really extraordinary.

We don’t have this yet in oncology, but we have the first drug that’s gone from an AI model detecting a new target, to AI designing a molecule that could hit that target, to phase 1, phase 2, and now a phase 3 trial. This is in pulmonary fibrosis. AI is already changing drug discovery, which can do nothing but benefit our patients; accelerating discovery, making it cheaper, democratizing it. The potential there is really incredible. We’ve talked about some of the clinical applications, but what do we lose? What I hear from people is: what is our career going to look like in 10 years, when AI is more mature and more robust? Are we going to be steering AI, or will AI be steering us? There’s a lot of misgivings and uncertainty, which makes sense. This is transformative, and transformation is both thrilling and unsettling.

Loaiza-Bonilla: Yes, and I always feel it’s about us becoming adaptable. That’s the resilience that comes along with that. There’s maybe this Jevons paradox, the idea that it’s not going to be a replacement, but [there may be] even more need for us, because we’re going to be able to spend more time making more diagnoses. We’re going to need more of us, and that’s why AI can help us. It becomes a collaborative intelligence; that’s my hope. I’m happy to be replaced if we can get rid of cancer forever.

Matasar: From your mouth to God’s ear.

Loaiza-Bonilla: Exactly. We’d all be happy to be replaced by any AI tool that can actually cure cancer, but we’re really far away from that. So for now, what we can do is use these tools to make the life of our patients better, and hopefully ours too. Less pajama time, so we can spend time with our patients and families.