Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions
11
Zitationen
4
Autoren
2025
Jahr
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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.357 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.221 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.640 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.482 Zit.