The Role of Artificial Intelligence in Gynecologic Oncology Decision-Making: A Feasibility Study
Open accessAbstract
Objectives: The objective of this study was to examine the potential of artificial intelligence (AI) in gynecologic oncology decision-making. Design: A feasibility study was conducted. Participants: Fictitious case vignettes of patients with gynecologic carcinomas were used. Setting: The setting was a fictive one. Methods: Fictitious case vignettes of gynecologic carcinomas were created and evaluated by physicians with varying levels of professional experience, as well as by language models including ChatGPT 4.0, Google Gemini, and Bing Copilot. Treatment approval decisions were based on standardized clinical and laboratory criteria. Results: Two cases of breast cancer, 1 case of ovarian cancer, 1 case of cervical cancer, and 1 case of endometrial cancer were evaluated. All three language models were able to evaluate all clinical cases and make therapy-relevant suggestions, with ChatGPT providing the most clear and concise recommendations that were in 3 cases totally consistent with physician assessments. Limitations: This study was limited to a feasibility study based on five fictitious case vignettes. Conclusions: The study demonstrates that AI models, such as ChatGPT, can to some extent evaluate clinical cases, recognize clinical and/or laboratory abnormalities, and make therapy-related suggestions. Despite high overall agreement, differences were predominantly noted in the more complex cases, rendering human interpretation necessary. The findings underscore the benefits of AI in terms of clarity, time efficiency, and cost-effectiveness. Future research should further explore the application of AI to real patient data and development of hybrid decision models to optimize integration into clinical practice.
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Psilopatis et al. demonstrated feasibility but noted discrepancies in complex cases.
“Psilopatis et al. demonstrated feasibility but noted discrepancies in complex cases [13]”
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Asoglu, H., Kose, S., Kayim, O., Abaci, A., Khatib, G., Vardar, M. A., Kilic Bagir, E., Gumurdulu, D., Kidi, M. M., Bayram, E., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15124451 · Cited by 1 · see citing works
“…versions and limited sample sizes: Meyer et al. evaluated GPT-4 in 60 gynecologic and senologic cases [12], Psilopatis et al. demonstrated feasibility but noted discrepancies in complex cases [13], and Gumilar et al. found variable accuracy across three LLMs on gynecologic cancer vignettes [14].”
- Authors
- Psilopatis, I., Sipulina, N., Stuebs, F. A., Heindl, F., Poeschke, P., Bader, S., Krueckel, A., Fasching, P. A., Beckmann, M. W., & Emons, J.
- Affiliations
- University of Basel (CH)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Corresponding author
- Psilopatis, Iason
- Venue
- Gynecologic and Obstetric Investigation
- Publisher
- S. Karger AG
- Published
- 2025-02-20
- Volume
- 90
- Issue
- 5
- Pages
- 483-491
- ISSN
- 0378-7346 (ISSN-L)
- 1423-002X
- PMC
- PMC12500289
- OpenAlex
- W4407765993
- Cited by
- 8
- References
- 6
- Citations by year
- 2026: 7 · 2025: 1
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Gynecologic and Obstetric Investigation (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, gynecologic oncology, clinical decision-making, large language models, ChatGPT, Google Gemini, gynecologic cancer, breast cancer, ovarian cancer, cervical cancer, endometrial cancer, physician experience, hybrid decision model
- MeSH terms
- Clinical Decision-Making, Artificial Intelligence, Decision Making, Feasibility Studies, Female, Genital Neoplasms, Female, Gynecology, Humans, Medical Oncology
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