GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions

Generative Pre-trained Transformer (GPT)-4, a versatile conversational artificial intelligence, has potential applications in medicine, but its ability to support physicians' decision-making remains unclear. We evaluated GPT-4's performance in assisting physicians with nephrology questions. Forty-five single-answer multiple-choice questions were extracted from the Core Curriculum in Nephrology articles published in the American Journal of Kidney Diseases from October 2021 to June 2023. Eight junior physicians without board certification and ten senior physicians with board certification answered these questions twice: first unaided, then with the opportunity to revise their answers based on GPT-4's outputs. GPT-4 correctly answered 77.8% of the questions. Before using GPT-4, junior physicians had a median (interquartile range) proportion of correct answers of 53.3% (48.3-53.3), senior physicians 65.6% (60.6-66.7). After GPT-4 support, the median proportion of correct answers significantly increased to 72.2% (68.3-76.1) for juniors and 75.6% (73.3-80.0) for seniors (p = 0.008, p = 0.004). The improvement was significantly higher for junior physicians (p = 0.017). However, Senior physicians showed a decreased proportion of correct answers in one of the clinical categories. GPT-4 significantly improved physicians' accuracy in nephrology, especially among less experienced physicians, but may have negative impacts in specific subfields. Careful consideration is required when using GPT-4 to support physicians' decision-making.

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Publication Details

Journal
Scientific Reports
Published
2025-05-02
DOI
https://doi.org/10.1038/s41598-025-99774-3
Citations
11
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
7.20
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article

GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions

Kenichiro Tanabe, Ryunosuke Noda, Daisuke Ichikawa, Yugo Shibagaki
11 citations
Scientific Reports
Artificial Intelligence in Healthcare and Education
7.20
article

GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions

Kenichiro Tanabe, Ryunosuke Noda, Daisuke Ichikawa, Yugo Shibagaki
article en
11 citations

Abstract

Generative Pre-trained Transformer (GPT)-4, a versatile conversational artificial intelligence, has potential applications in medicine, but its ability to support physicians' decision-making remains unclear. We evaluated GPT-4's performance in assisting physicians with nephrology questions. Forty-five single-answer multiple-choice questions were extracted from the Core Curriculum in Nephrology articles published in the American Journal of Kidney Diseases from October 2021 to June 2023. Eight junior physicians without board certification and ten senior physicians with board certification answered these questions twice: first unaided, then with the opportunity to revise their answers based on GPT-4's outputs. GPT-4 correctly answered 77.8% of the questions. Before using GPT-4, junior physicians had a median (interquartile range) proportion of correct answers of 53.3% (48.3-53.3), senior physicians 65.6% (60.6-66.7). After GPT-4 support, the median proportion of correct answers significantly increased to 72.2% (68.3-76.1) for juniors and 75.6% (73.3-80.0) for seniors (p = 0.008, p = 0.004). The improvement was significantly higher for junior physicians (p = 0.017). However, Senior physicians showed a decreased proportion of correct answers in one of the clinical categories. GPT-4 significantly improved physicians' accuracy in nephrology, especially among less experienced physicians, but may have negative impacts in specific subfields. Careful consideration is required when using GPT-4 to support physicians' decision-making.

Scientific ReportsVol. 15(1)
St. Marianna University School of Medicine (JP)
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Artificial Intelligence in Healthcare and Education
7.20
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GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions — Kenichiro Tanabe, Ryunosuke Noda, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS