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2024 article

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

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1Pays d’affiliation déclarés

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Einleitung 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). Material und Methodik 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. Ergebnisse 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 and processing. Zusammenfassung 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. Publication History Article published online: 14 June 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
ChatGPT’s performance in German OB/GYN exams – paving the way for AI-enhanced medical education and clinical practice
Date Crossref
01/06/2024
Éditeur
Georg Thieme Verlag KG
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

  • TUM Klinikum pays non établi dans la notice
    Établissement de santé
  • Friedrich-Alexander-Universität Erlangen-Nürnberg pays non établi dans la notice
    Université ou école supérieure
  • University Hospital Bonn pays non établi dans la notice
    Établissement de santé
  • Heidelberg University pays non établi dans la notice
    Université ou école supérieure
  • University Hospital Heidelberg pays non établi dans la notice
    Établissement de santé
  • Technical University Munich (TU) Klinikum rechts der Isar pays non établi dans la notice
    Université ou école supérieure
  • Friedrich–Alexander-University Erlangen–Nuremberg (FAU) pays non établi dans la notice
    Université ou école supérieure
  • Bonn University Hospital pays non établi dans la notice
    Université ou école supérieure

TUM Klinikum, Friedrich-Alexander-Universität Erlangen-Nürnberg et University Hospital Bonn, avec 5 autres affiliations.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic SkillsRadiomics and Machine Learning in Medical Imaging

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