Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.

Autor:	Shyr C; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Grout RW; Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 46202, United States.; Regenstrief Institute, Inc, Indianapolis, IN 46202, United States., Kennedy N; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Akdas Y; Division of Emergency Medicine Research, Vanderbilt University Medical Center, Nashville, TN 37232, United States., Tischbein M; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Milford J; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Tan J; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Quarles K; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Edwards TL; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Novak LL; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States., White J; Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37240, United States., Wilkins CH; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States., Harris PA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Oct 01; Vol. 31 (10), pp. 2294-2303.
DOI:	10.1093/jamia/ocae186
Abstrakt:	Objective: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale. Materials and Methods: We engineered prompts to summarize abstracts at a literacy level accessible to the public, prioritizing succinctness, clarity, and practical relevance. Researchers and volunteers assessed ChatGPT-generated lay summaries across five dimensions: accuracy, relevance, accessibility, transparency, and harmfulness. We used precision analysis and adaptive random sampling to determine the optimal number of summaries for evaluation, ensuring high statistical precision. Results: ChatGPT achieved 95.9% (95% CI, 92.1-97.9) accuracy and 96.2% (92.4-98.1) relevance across 192 summary sentences from 33 abstracts based on researcher review. 85.3% (69.9-93.6) of 34 volunteers perceived ChatGPT-generated summaries as more accessible and 73.5% (56.9-85.4) more transparent than the original abstract. None of the summaries were deemed harmful. We expanded ResearchMatch's technical infrastructure to automatically generate and display lay summaries for over 750 published studies that resulted from the platform's recruitment mechanism. Discussion and Conclusion: Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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