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Exploring the potential of AI-powered applications for clinical decision-making in gynecologic oncology.
Int. J. Gynecol. Obstet., DOI: 10.1002/ijgo.70251 (2025)
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Chatgpt ; Breast Cancer ; Clinical Decision‐making ; Gynecology ; Large Language Model ; Tumor Board
Language
english
Publication Year
2025
HGF-reported in Year
2025
ISSN (print) / ISBN
0020-7292
e-ISSN
1879-3479
Publisher
Wiley
Publishing Place
111 River St, Hoboken 07030-5774, Nj Usa
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503800-001
Grants
Projekt DEAL
WOS ID
001507615400001
Scopus ID
105008227963
PubMed ID
40512143
Erfassungsdatum
2025-06-30