Evidence map›Paper›PMID 38155661›Full record

ArticleFrontiers in medicine2023

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

Maximilian Riedel, Katharina Kaefinger, Antonia Stuehrenberg, Viktoria Ritter, Niklas Amann, Anna Graf, Florian Recker, Evelyn Klein, Marion Kiechle, Fabian Riedel and 1 more

Abstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  4. Article
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  7. Medical education in obstetrics and gynecology: A global update from 2025.Acta obstetricia et gynecologica Scandinavica · 2026
    Review
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  10. Exploring the potential of AI-powered applications for clinical decision-making in gynecologic oncology.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
    Article
  11. Review
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  15. Can ChatGPT Provide Patient-Friendly and Reliable Information on Cervical Cancer Screening? A Study of ChatGPT-Generated Information in Polish.Medical science monitor : international medical journal of experimental and clinical research · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Maximilian RiedelDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Katharina KaefingerDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Antonia StuehrenbergDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Viktoria RitterDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Niklas AmannDepartment of Gynecology and Obstetrics, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany.
Anna GrafDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Florian ReckerDepartment of Gynecology and Obstetrics, Bonn University Hospital, Bonn, Germany.
Evelyn KleinDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Marion KiechleDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
Fabian RiedelDepartment of Gynecology and Obstetrics, Heidelberg University Hospital, Heidelberg, Germany.
Bastian MeyerDepartment of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceChatGPTmachine learningmedical educationobstetrics and gynecologystudents

Identifiers

PMID38155661
PMCPMC10753765

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.