Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions

Shiyao Xie; Wenjing Zhao; Guanghui Deng; Guohua He; Na He; Zhenhua Lu; Weihua Hu; Mingming Zhao; Jian Du

doi:10.1093/jamia/ocae100

Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions

J Am Med Inform Assoc. 2024 May 17:ocae100. doi: 10.1093/jamia/ocae100. Online ahead of print.

Authors

Shiyao Xie^{1

2}, Wenjing Zhao^{1

2}, Guanghui Deng³, Guohua He⁴, Na He⁵, Zhenhua Lu⁶, Weihua Hu⁷, Mingming Zhao⁸, Jian Du^{1

2}

Affiliations

¹ Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China.
² National Institute of Health Data Science, Peking University, Beijing, 100191, China.
³ School of Health Humanities, Peking University, Beijing, 100191, China.
⁴ Department of Pediatric Nephrology and Rheumatology, Sun Yat-sen University First Affiliated Hospital, Guangzhou, Guangdong, 510062, China.
⁵ Department of Pharmacy, Peking University Third Hospital, Beijing, 100089, China.
⁶ Department of Gastrointestinal Cancer Translational Research Laboratory, Peking University Cancer Hospital, Beijing, 100143, China.
⁷ Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
⁸ Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, 100089, China.

PMID: 38758667
DOI: 10.1093/jamia/ocae100

Abstract

Objective: Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPT as a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research.

Materials and methods: We evaluated ChatGPT's effectiveness in identifying conflicting evidence and investigated its principles of logical reasoning. An automated framework was developed to generate a PubMed dataset focused on controversial clinical topics. ChatGPT analyzed this dataset to identify consensus and controversy, and to formulate unsolved research questions. Expert evaluations were conducted 1) on the consensus and controversy for factual consistency, comprehensiveness, and potential harm and, 2) on the research questions for relevance, innovation, clarity, and specificity.

Results: The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. Notably, without explicit reasoning prompts, ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. ChatGPT's conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. The research questions proposed by ChatGPT received high expert ratings.

Discussion: Our experiment implies that, in evaluating the relationship between evidence and claims, ChatGPT considered more detailed information beyond a straightforward assessment of sentimental orientation. This ability to process intricate information and conduct scientific reasoning regarding sentiment is noteworthy, particularly as this pattern emerged without explicit guidance or directives in prompts, highlighting ChatGPT's inherent logical reasoning capabilities.

Conclusion: This study demonstrated ChatGPT's capacity to evaluate and interpret scientific claims. Such proficiency can be generalized to broader clinical research literature. ChatGPT effectively aids in facilitating clinical studies by proposing unresolved challenges based on analysis of existing studies. However, caution is advised as ChatGPT's outputs are inferences drawn from the input literature and could be harmful to clinical practice.

Keywords: ChatGPT; clinical research; inconsistent evidence; large language model; reasoning.

Abstract

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