More than 450 registrants from across Beth Israel Lahey Health (BILH) attended the 2025 Artificial Intelligence and Machine Learning Symposium, held at Harvard Medical School's Joseph B. Martin Center on February 10. The event brought together researchers, clinicians, and technology leaders to explore the growing role of AI and ML in patient care, research, and medical education.
Gyongyi Szabo, MD, PhD, Chief Academic Officer of BILH, said the symposium offered a platform to bridge gaps between technology, research, and patient care. Kevin Tabb, MD, President and CEO of BILH, welcomed attendees by video, noting the event reflected how departments and organizations across BILH are coming together around a shared academic mission.
Keynote speaker Peter Szolovits, PhD, of MIT's Computer Science and Artificial Intelligence Laboratory, reflected on five decades in the field, tracing AI's evolution from early single-disease diagnostic models to today's work in speech-to-text documentation and ICU data analysis.
Two expert panels followed, one on responsibly implementing AI and ML in healthcare, and one on how BILH community members can launch their own AI and ML projects. Panelists discussed data privacy, bias mitigation, and practical steps for getting a project off the ground, drawing on real examples from BIDMC clinicians already working with BILH's Innovation Lab. The day closed with a keynote from Rowland Illing, MD of Amazon Web Services on the role of cloud infrastructure in scaling AI and ML tools in healthcare.
A poster competition ran alongside the program, with a BILH-wide scientific committee judging fifty digital poster presentations across four categories.
Radiology Poster Highlights Framework for AI Implementation
Among the poster presentations was a project from Lahey Radiology, led by principal investigator Jalil Afnan and presented by Andrew Lee. The poster, "Implementing AI/ML in Radiology: Enhancing Patient-Centered Care and Clinical Workflow," outlined a roadmap for integrating AI and ML tools into radiology operations.
The project drew on specific AI tools already deployed at Lahey Radiology, highlighting measurable improvements in both patient-centered care and operational efficiency. Afnan and Lee presented a four-step framework for teams considering similar implementations, along with technical hurdles encountered along the way and the approach used to navigate them.