The timely and effective return of aggregate study results is essential to informing the public about clinical findings and promotes trust and transparency in the research process. This session will review leveraging OpenAI’s ChatGPT-4 to generate lay summaries and implementing them on ResearchMatch, a national clinical study recruitment registry, to enable cost-effective and scalable return of study results. Cathy Shyr from Recruitment Innovation Center will describe the process of operationalizing this approach, including prompt engineering, study design, and rigorous evaluation of researchers’ and volunteers’ perception of AI-generated summaries. In addition, the speaker will share lessons learned, including opportunities and challenges of using AI to enhance research accessibility and transparency.

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This event,  hosted by the Trial Innovation Network, is available to the University of Rochester community via the UR Clinical and Translational Science Institute.