Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic...

Background: The reliability of large language models (LLMs) in complex oncologic decision-making remains inadequately validated. This study evaluated the concordance of ChatGPT-5 with multidisciplinary tumor board (MDT) decisions in gynecologic oncology, assessing accuracy, reproducibility, and domains of discordance. Methods: We analyzed 242 gynecologic cancer cases (endometrial n = 102, ovarian n = 85, cervical n = 40, rare n = 15) discussed at the Çukurova University Gynecologic Oncology MDT (2024-2025). Standardized clinical summaries were input into ChatGPT-5 using a structured prompt template. Each case was queried three times within a single calendar day using independent conversations. Recommendations were evaluated by two blinded medical oncologists using a 5-point Likert scale. A composite performance score (CPS) was calculated as (mean Likert/5) × 100. Concordance was analyzed using Cohen's kappa (κ). Results: Inter-rater reliability was substantial to almost perfect for both MDT (κ = 0.761) and AI (κ = 0.814) evaluations (both p < 0.001). MDT-AI concordance was fair (Rater 1: κ = 0.258; Rater 2: κ = 0.334). CPS were significantly higher for MDT versus AI (Rater 1: 93.8% ± 5.2 vs. 89.4% ± 6.7; Rater 2: 93.4% ± 5.5 vs. 89.7% ± 6.4; both p < 0.001). Full consistency across three queries was achieved in only 37.2% of cases (90/242). AI performance was significantly inferior in advanced-stage disease (p = 0.008), genetic testing (p = 0.006), fertility-sparing (p = 0.018), and novel therapeutics (p = 0.003). Conclusions: ChatGPT-5 demonstrates potential as a clinical decision support tool but lacks sufficient reliability for independent use. Key limitations include inconsistency in 62.8% of cases, suboptimal performance in advanced-stage disease, and deficiencies in precision oncology domains. These findings suggest that human expertise remains indispensable for the individualized management of complex gynecologic malignancies.