Riding the New Wave of AI in Health Care

The Icahn School of Medicine at Mount Sinai and the New York Academy of Sciences jointly hosted a conference, “The New Wave of AI in Health Care,” on May 12 and May 13, 2026.

The health care industry has overwhelmingly embraced artificial intelligence (AI) technology in all aspects—clinical, academic, administrative, and more—and with the breakneck speed of progress in this area, it’s little wonder the field is seeing an outpouring of innovation.

Over the two days, attendees gleaned the latest ways AI is being used to rethink health care delivery, clinical workflows, drug discovery, and patient experience and outcomes. More importantly, the event served as an opportunity for networking, creating the foundations for future collaboration. The event attracted clinicians, academics, industry professionals, and news media from around the world, with renowned institutions participating, including Mayo Clinic, Epic, AstraZeneca, and the National Institutes of Health (NIH).

“Oftentimes you go to conferences and the real experience is what happens between the talks—the one-on-one conversations you have with everyone,” said Alexander Charney, MD, PhD, Vice Chair of AIHH, during his closing remarks at the event.

The gathering of so many great minds together in one space to collectively ask questions about AI and solve problems in health care is inspiring, said Dr. Charney in his speech.

“At times, I was thinking about how people in the future would look at us today, and how people in the past would look at us today—in this room, dealing with this challenge of our time of how artificial intelligence is going to be used in health care and taking care of people,” he said. “And where I land on how we move forward, is that we should try and make both of those groups of people proud.”

What is this new wave of AI in the health care industry? Dr. Nadkarni, who hosted a keynote session, shares his perspective on the development of the field and his thoughts on the conference. A video recording of his session can also be viewed below.

Session VII: Keynote and Learning Health System. Session includes a fireside chat with former New York City Health Commissioner Dave Chokshi, MD, and a presentation from a member of NIH.

What is this “new wave of AI” we are seeing in health care? What makes it different from other technological waves we’ve seen before?

It’s not the first such wave—AI in medicine goes back to the expert systems of the 1970s and the machine learning wave of the last 15 years. What’s new is large, general-purpose, multimodal models at unprecedented scale. Earlier tools were narrow and brittle; this wave is general and fluent. The same model can read a note, interpret a scan, and reason across both. And it has crossed out of the lab into the clinic, touching the actual practice of medicine. That’s why it feels less like a better tool and more like a genuine shift.

How often do events such as this occur to bring innovators together? What would you say are some of the biggest values of such conferences?

They’re getting more frequent, but the ones that matter are still rare, because the hard part isn’t gathering people, it’s gathering the right mix. The value is putting clinicians, computer scientists, ethicists, regulators, and patients in one room. It separates signal from hype, builds the relationships that later become multisite studies, and forces the hard questions about equity and implementation onto the main stage. Honestly, the hallway conversations matter as much as the talks.

What does equity in health care AI look like, and how can we deliver the power of those tools to communities who need those solutions the most?

Equity doesn’t happen by default. Models built on well-resourced data tend to work worst for those already underserved. Conferences can help by refusing to treat that as an afterthought, by putting equity on the main stage, connecting innovators with safety-net and global-health partners—our work with the Guyana Ministry of Health is one example—and sharing tools openly so smaller institutions aren’t locked out.

AI’s real promise is scale. A validated model can reach a rural clinic as easily as an academic center, but only if we design for that from the start.

What sort of pace should academic health systems like Mount Sinai be adopting for this wave of AI? Should systems like ours be at the forefront and setting directions?

Fast enough to lead, deliberate enough to be safe. Academic systems have the data, the patients, the rigor, and the mandate to generate evidence rather than just byproducts—so yes, we should be at the forefront. Our job is to set the standard and give the field a trustworthy template to follow. Leading here means leading on rigor and equity, not simply being first.

Lastly, in reflecting on Dr. Charney’s closing remarks, what are your thoughts on how the world of the past and the world of the future would think about what we’re doing with AI in health care today?

Clinicians a generation ago would be astonished that a machine can draft a note or read a scan—and probably wary about what it means for the human relationship at the center of medicine.

The future, I think, will judge us not on how clever our models were, but on whether we used them wisely, safely, and fairly, and kept the patient and clinician at the center. Echoing Dr. Charney, my hope is they’ll see this as the moment we chose to augment human care rather than replace it. We’re not just building tools—we’re setting precedents.