Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness

Gongbo Zhang; Qiao Jin; Denis Jered McInerney; Yong Chen; Fei Wang; Curtis L. Cole; Qian Yang; Yanshan Wang; Bradley A. Malin; Mor Peleg; Byron C. Wallace; Zhiyong Lu; Chunhua Weng; Yifan Peng

doi:10.1016/j.jbi.2024.104640

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness

Gongbo Zhang, Qiao Jin, Denis Jered McInerney, Yong Chen, Fei Wang, Curtis L. Cole, Qian Yang, Yanshan Wang, Bradley A. Malin, Mor Peleg, Byron C. Wallace, Zhiyong Lu, Chunhua Weng, Yifan Peng

Department of Information Systems

Research output: Contribution to journal › Article › peer-review

Abstract

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Original language	English
Article number	104640
Journal	Journal of Biomedical Informatics
Volume	153
DOIs	https://doi.org/10.1016/j.jbi.2024.104640
State	Published - May 2024

Bibliographical note

Publisher Copyright:
© 2024

Keywords

Evidence-based medicine
Large language models
Medical evidence summarization
Trustworthy generative AI

ASJC Scopus subject areas

Health Informatics
Computer Science Applications

Access to Document

10.1016/j.jbi.2024.104640

Cite this

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title = "Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness",

abstract = "Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.",

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year = "2024",

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doi = "10.1016/j.jbi.2024.104640",

language = "English",

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AU - Zhang, Gongbo

AU - Jin, Qiao

AU - Jered McInerney, Denis

AU - Chen, Yong

AU - Wang, Fei

AU - Cole, Curtis L.

AU - Yang, Qian

AU - Wang, Yanshan

AU - Malin, Bradley A.

AU - Peleg, Mor

AU - Wallace, Byron C.

AU - Lu, Zhiyong

AU - Weng, Chunhua

AU - Peng, Yifan

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AB - Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

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Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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