Evidence map›Paper›PMID 40510580›Full record

ArticleFrontiers in public health2025

Exploring the medical ethical limitations of GPT-4 in clinical decision-making scenarios: a pilot survey.

Yu-Tao Xiong, Yu-Min Zeng, Hao-Nan Liu, Ya-Nan Sun, Wei Tang, Chang Liu

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yu-Tao Xiong *State Key Laboratory of Oral Diseases and National Center for Stomatology and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Yu-Min Zeng *State Key Laboratory of Oral Diseases and National Center for Stomatology and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Hao-Nan LiuState Key Laboratory of Oral Diseases and National Center for Stomatology and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Ya-Nan SunMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
Wei TangState Key Laboratory of Oral Diseases and National Center for Stomatology and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Chang LiuState Key Laboratory of Oral Diseases and National Center for Stomatology and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aims to conduct an examination of GPT-4's tendencies when confronted with ethical dilemmas, as well as to ascertain their ethical limitations within clinical decision-makings. Methods: Ethical dilemmas were synthesized and organized into 10 different scenarios. To assess the responses of GPT-4 to these dilemmas, a series of iterative and constrained prompting methods were employed. Custom questionnaire analysis and principal adherence analysis were employed to evaluate the GPT-4-generated responses. Questionnaire analysis was used to assess GPT-4's ability to provide clinical decision-making recommendations, while principal adherence analysis evaluated its alignment with to ethical principles. Error analysis was conducted on GPT-4-generated responses. Results: The questionnaire analysis (5-point Likert scale) showed GPT-4 achieving an average score of 4.49, with the highest scores in the Physical Disability scenario (4.75) and the lowest in the Abortion/Surrogacy scenario (3.82). Furthermore, the principal adherence analysis showed GPT-4 achieved an overall consistency rate of 86%, with slightly lower performance (60%) observed in a few specific scenarios. Conclusion: At the current stage, with the appropriate prompt techniques, GPT-4 can offer proactive and comprehensible recommendations for clinical decision-making. However, GPT-4 exhibit certain errors during this process, leading to inconsistencies with ethical principles and thereby limiting its deeper application in clinical practice.

Indexed as

Clinical Decision-MakingEthics, MedicalAdultFemaleHumansMalePilot ProjectsSurveys and Questionnairesartificial intelligencedecision-makingethical medicalGPT-4LLMs

Identifiers

PMID40510580
PMCPMC12159065

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.