Some of the brightest minds in AI gathered in person at Johns Hopkins University’s Data Science and AI Institute for a convening of the Johns Hopkins Workgroup on AI and Healthcare. The group unites faculty, researchers, and clinicians from across Johns Hopkins University — the schools of Medicine and Nursing, the Bloomberg School of Public Health, the Whiting School of Engineering, and Carey Business School, plus DSAI.
Hopkins Business of Health Initiative Director Dan Polsky explained that across the university, people are working toward making the healthcare system better, and convenings such as this one are a crucial part of the work.
“Bringing people together from across those domains can make the whole greater than the sum of its parts," he said.
The Workgroup on AI and Healthcare, founded and co-chaired by Associate Professor of Pediatrics Risa Wolf, MD, and Bernard T. Ferrari Professor of Business Tinglong Dai, PhD, has been meeting on Zoom since January 2023, but this was the first chance to do so face to face. And face to face was the whole point.
Throughout the morning, Wolf and Dai reminded attendees that the goal of meeting was to talk to each other. In doing so, they explained, attendees would help build a unified medical AI community at Hopkins by understanding what colleagues are working on, and by exploring new collaborations.
The irony was hard to miss. While their work focuses on building ever-smarter machines, progress in AI continues to rely on something uniquely human—the conversations, shared curiosity, and trust that emerge when intelligent minds meet in person.
Ten ideas making impact
Ten speakers presented in lightning rounds in two sessions: one about AI in clinical care, the other about AI across the health ecosystem.
DSAI Director Mark Dredze underscored the importance of Hopkins’ leadership in these areas.
“The gap between AI that’s technically impressive and AI that’s actually clinically trustworthy is critical – and Hopkins has to be a leader in that space.”
“The gap between AI that’s technically impressive and AI that’s actually clinically trustworthy is critical – and Hopkins has to be a leader in that space,” he said.
The event was selected for funding by the DSAI as part of its inaugural Events Support Funding Program.
Wolf, whose research focuses on diabetes care and the practical introduction of AI into clinical workflows, chaired a session that covered topics close to the bedside: clinical decisions; diagnostic safety; human–AI interaction in care; behavioral and decision frameworks; and the long arc of biomedical informatics. Speakers presented compelling research about patient trust, nursing data in AI, conversational AI agents, and verifying workflows.
Harold Lehmann, a professor of biomedical informatics and data science at the Johns Hopkins School of Medicine, put the potential fairly simply.
“Decision models are incredibly powerful and consistent,” he said. “And if we can finally get them into practice, they can help us make better decisions in medicine.”
Dai, an operations and analytics scholar working at the intersection of AI, healthcare delivery, and the economics of care at Johns Hopkins Carey Business School, chaired a session that widened the lens with talks about agentic AI on the frontier of mental health; AI in molecular discovery; policy modeling tied to outcomes; AI in health IT and operations; and the economics of value, access, and trust. With examples of AI innovations from cataract surgery to diabetes management and psychiatric care, these speakers balanced optimism for healthcare AI with an honest assessment of its challenges.
Perhaps the first challenge is turning concepts into concrete operations, which is a focus of the Innovation Translation Council at the Johns Hopkins Bloomberg School of Public Health, an initiative designed to accelerate the commercialization of cutting-edge research into real-world, self-sustaining products, services, and entrepreneurial ventures. Anthony Leung chairs the council.
“AI is compressing the translation cycle,” he said. “Whoever builds that capacity now can seize the moment to turn ideas into real public-health impact.”
Making progress
At the conclusion of the presentations, with ideas and inspiration tangible in the room, the co-chairs went back to the business of networking, encouraging people to find someone they hadn’t yet met and introduce themselves. This is the moment the convening pays off - in conversations Zoom could never produce.
In an age defined by algorithms, automation, and machine learning, the most important breakthroughs in artificial intelligence may still begin the old-fashioned way: with people meeting face to face and shaking hands.
