OnDemand: AI for Obesity Research and Clinical Practice-Building Effective AI Obesity Teams-Clinical Viewpoint

Video Transcription

Video Summary

Leanne Redman explains that effective AI-clinical teams require much more than one expert or good data. Using her long collaboration with Diana Thomas as an example, she describes how a dynamic energy balance model was built from clinical data to predict individual weight loss, later scaled into apps used in NIH studies. Her main message: successful AI work depends on strong team building.<br /><br />She offers five tips: have a clear prompt and vision; put the right people in the right roles; engage and teach the whole team continuously; remove roadblocks and ensure informed decision-making; and keep meeting frequently so the team can achieve real results. She compares effective teams to an orchestra, where every instrument family matters and the conductor must keep everyone aligned.<br /><br />In the Q&A, she tells clinicians they do not need to be computer scientists to work with AI, but they should understand the clinical question and partner with experts. She warns against using AI to write grants or fabricate references, and recommends using it to refine language after drafting. The session also addresses privacy, HIPAA, access, environmental cost, and the need to test AI tools carefully before clinical use.

Asset Subtitle

By Leanne M. Redman, PhD, FTOS

Keywords

AI-clinical teams

team building

dynamic energy balance model

weight loss prediction

clinical data

NIH studies

HIPAA privacy