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Meyer, B.* ; Kfuri-Rubens, R. ; Schmidt, G.* ; Tariq, M.* ; Riedel, C.* ; Recker, F.* ; Riedel, F.* ; Kiechle, M.* ; Riedel, M.J.*

Exploring the potential of AI-powered applications for clinical decision-making in gynecologic oncology.

Int. J. Gynecol. Obstet., DOI: 10.1002/ijgo.70251 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
OBJECTIVE: The rise of artificial intelligence (AI) and large language models like Llama, Gemini, or Generative Pretraining Transformer (GPT) signals a promising new era in natural language processing and has significant potential for application in medical care. This study seeks to investigate the potential of GPT-4 for automated therapy recommendations by examining individual patient health record data with a focus on gynecologic malignancies and breast cancer. METHODS: We tasked GPT-4 with generating independent treatment proposals for 60 randomly selected patient cases presented at gynecologic and senologic multidisciplinary tumor boards (MDTs). The treatment recommendations by GPT-4 were compared with those of the MDTs using a novel clinical concordance score and were reviewed both qualitatively and quantitatively by experienced gynecologic oncologists. RESULTS: GPT-4 generated coherent therapeutic recommendations for all clinical cases. Overall, these recommendations were assessed by clinical experts as moderately sufficient for real-word clinical application. Deficiencies in both accuracy and completeness were especially noted. Using a quantitative clinical concordance score, GPT-4 consistently demonstrated superior performance in managing the senologic cases compared with the gynecologic cases. Iterative prompting substantially enhanced treatment recommendations in both categories, increasing concordance with MDT decisions to up to 84% in senologic cases. CONCLUSION: GPT-4 is capable of processing complex patient cases and generates detailed treatment recommendations; however, differences persist in surgical approaches and the use of systemic therapies, and there is a tendency toward recommending excessive genetic testing. As AI-powered solutions continue to be integrated into medicine, we envision the potential for automated therapy recommendations to play a supportive role in human clinical decision-making in the future.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Chatgpt ; Breast Cancer ; Clinical Decision‐making ; Gynecology ; Large Language Model ; Tumor Board
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0020-7292
e-ISSN 1879-3479
Zeitschrift International Journal of Gynecology and Obstetrics
Verlag Wiley
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Projekt DEAL
DOI 10.1002/ijgo.70251
WOS ID 001507615400001
Scopus ID 105008227963
PubMed ID 40512143
Erfassungsdatum 2025-06-30
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