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ChatGPT's performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice.

作者信息

Riedel Maximilian, Kaefinger Katharina, Stuehrenberg Antonia, Ritter Viktoria, Amann Niklas, Graf Anna, Recker Florian, Klein Evelyn, Kiechle Marion, Riedel Fabian, Meyer Bastian

机构信息

Department of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.

Department of Gynecology and Obstetrics, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany.

出版信息

Front Med (Lausanne). 2023 Dec 13;10:1296615. doi: 10.3389/fmed.2023.1296615. eCollection 2023.


DOI:10.3389/fmed.2023.1296615
PMID:38155661
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10753765/
Abstract

BACKGROUND: Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial learning and large language model tool developed by OpenAI in 2022. It utilizes deep learning algorithms to process natural language and generate responses, which renders it suitable for conversational interfaces. ChatGPT's potential to transform medical education and clinical practice is currently being explored, but its capabilities and limitations in this domain remain incompletely investigated. The present study aimed to assess ChatGPT's performance in medical knowledge competency for problem assessment in obstetrics and gynecology (OB/GYN). METHODS: Two datasets were established for analysis: questions (1) from OB/GYN course exams at a German university hospital and (2) from the German medical state licensing exams. In order to assess ChatGPT's performance, questions were entered into the chat interface, and responses were documented. A quantitative analysis compared ChatGPT's accuracy with that of medical students for different levels of difficulty and types of questions. Additionally, a qualitative analysis assessed the quality of ChatGPT's responses regarding ease of understanding, conciseness, accuracy, completeness, and relevance. Non-obvious insights generated by ChatGPT were evaluated, and a density index of insights was established in order to quantify the tool's ability to provide students with relevant and concise medical knowledge. RESULTS: ChatGPT demonstrated consistent and comparable performance across both datasets. It provided correct responses at a rate comparable with that of medical students, thereby indicating its ability to handle a diverse spectrum of questions ranging from general knowledge to complex clinical case presentations. The tool's accuracy was partly affected by question difficulty in the medical state exam dataset. Our qualitative assessment revealed that ChatGPT provided mostly accurate, complete, and relevant answers. ChatGPT additionally provided many non-obvious insights, especially in correctly answered questions, which indicates its potential for enhancing autonomous medical learning. CONCLUSION: ChatGPT has promise as a supplementary tool in medical education and clinical practice. Its ability to provide accurate and insightful responses showcases its adaptability to complex clinical scenarios. As AI technologies continue to evolve, ChatGPT and similar tools may contribute to more efficient and personalized learning experiences and assistance for health care providers.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/494a/10753765/5505ef6b3fef/fmed-10-1296615-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/494a/10753765/5505ef6b3fef/fmed-10-1296615-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/494a/10753765/5505ef6b3fef/fmed-10-1296615-g001.jpg

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[1]
ChatGPT's performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice.

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[5]
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[6]
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[10]
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本文引用的文献

[1]
Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma.

Int J Mol Sci. 2023-10-18

[2]
ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model.

Int J Oral Sci. 2023-7-28

[3]
Application of ChatGPT in Routine Diagnostic Pathology: Promises, Pitfalls, and Potential Future Directions.

Adv Anat Pathol. 2024-1-1

[4]
Artificial Intelligence in Ophthalmology: A Comparative Analysis of GPT-3.5, GPT-4, and Human Expertise in Answering StatPearls Questions.

Cureus. 2023-6-22

[5]
Performance of a Large Language Model on Practice Questions for the Neonatal Board Examination.

JAMA Pediatr. 2023-9-1

[6]
Small groups, big possibilities: radical pedagogical approaches to critical small-group learning in medical education.

Can Med Educ J. 2023-4-8

[7]
ChatGPT failed Taiwan's Family Medicine Board Exam.

J Chin Med Assoc. 2023-8-1

[8]
ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations.

Front Artif Intell. 2023-5-4

[9]
GPT-4: a new era of artificial intelligence in medicine.

Ir J Med Sci. 2023-12

[10]
Overview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts.

Cureus. 2023-4-8

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