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AI in Medicine: Collaborating on Challenges and Opportunities

By Mallory Locklear, PhD
5 Minute Read

The world of AI is still quite new and rapidly changing. Opportunities abound when it comes to how AI can be applied and the discoveries to be made. There’s also a lot to be gained through collaboration, especially for fields like medical AI where data can often be siloed and privacy is of utmost importance.

Yale School of Medicine (YSM) wants to develop a community around medical AI and establish a forum for conversations on these important, timely topics. On March 26, YSM will convene researchers, clinicians, informaticians, and data scientists to talk about where medical AI is, where it’s going, and the opportunities for collaboration and shared knowledge.

The 2026 Yale Medical AI Symposium is hosted by the Yale Center for Clinical Investigation and the Yale Biomedical Informatics and Computing core. It will feature a keynote address, presentations, a panel discussion on multi-site collaboration, and a poster session. Around 300 people have registered to date, and registration is still open for those who want to join.

Lucila Ohno-Machado, MD, PhD, MBACredit: Robert Lisak

“We want to hear about the exciting research happening here at Yale and at other institutions, we want to discuss best practices for important issues like developing, evaluating, and deploying AI in translational research—that is, research that is close to being applied in healthcare practice—and we want to kick off collaborations that can advance the work we’re all doing,” says Lucila Ohno-Machado, MD, PhD, Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science, deputy dean for biomedical informatics, and chair of the Department of Biomedical Informatics and Data Science (BIDS).

One of the aims of the symposium is to connect research groups across Yale’s campus and connect Yale with other institutions.

“Those of us working in medical AI need a platform where we can come together to share ideas and discuss best practices,” says Hua Xu, PhD, Robert T. McCluskey Professor of Biomedical Informatics and Data Science at YSM and co-organizer of the symposium. “That’s why we’re bringing groups together in this forum.”

We talked to Ohno-Machado and Xu about the event and what they hope it accomplishes. The interview below has been edited and condensed for clarity.

What were your goals when designing this symposium?

Hua Xu, PhD: The main goal is to foster collaboration, in several ways. AI is still relatively new in medicine. For example, institutions are starting to develop appropriate policies and governance structures for developing and implementing medical AI applications. But rather than every medical institution developing its own governance framework in isolation, it makes much more sense for us to come together, share experiences, and think through these challenges collectively.

Lucila Ohno-Machado, MD, PhD: Another collaboration is around the work we all do and the trust that other people have in our institutions. Clinical and genome data must be protected. It’s sensitive data from people whose privacy is of the highest importance. Individual institutions can protect that data and at the same time use it for important medical AI research if certain precautions are taken.

AI research requires a lot of data. And we’ll all learn so much more if we can combine all of our data into a larger, more representative pool. BIDS is leading work here on what are called “federated models,” which can be trained on data housed at different institutions without moving the data itself. This means much more data are used to train a model, but in a way that doesn’t compromise the privacy of that data. Researchers such as Hoon Cho, PhD, are specifically focused on these privacy enhancing technologies for AI.

How will working together advance research?

Ohno-Machado: If we can bring together other schools or institutions through federated models, that would enhance all of our understanding. If our different collections of data can be safely brought together, it means we can ask more questions and learn more deeply about what we’re studying. And for rare diseases, where no one institution will have a lot of data, combining forces could be transformational.

So the symposium is built around a number of themes that could highlight where different groups might work together.

What qualities or infrastructure make for a good medical AI collaboration?

Xu: One clear opportunity for medical AI is collaboration among CTSA sites. These are medical institutions that are part of the National Institutes of Health’s Clinical and Translational Science Award (CTSA) program, which is a national network with a shared goal of accelerating the translation of research discoveries into improved patient care.

Yale is a CTSA hub and we’re interested in collaborating with other CTSA hubs in our region. The symposium is a space for all of us to start those conversations.

Hua Xu, PhD

What are the presentation and discussion themes?

Xu: We will have speakers talking about AI in clinical practice, such as how to apply it to specific disease areas; methodology, including large language models and AI agents; imaging and multimodal data; and ethics and privacy issues. We’ll also have a panel discussion on how CTSAs can collaborate on medical AI research. Then we’ll wrap up with a reception and poster presentations.

Can people still sign up to participate?

Ohno-Machado: Yes, anyone—faculty, trainees, anyone interested from Yale or elsewhere—can register on our website. Anyone interested in presenting a poster can also submit an application by March 6.

The 2026 Yale Medical AI Symposium will take place March 26, 8 a.m. - 4 p.m., followed by a reception. It will be held at 101 College St. in New Haven with a remote option for those you cannot attend in person.

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Mallory Locklear, PhD
Managing Editor—Science, Research, and Education

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