Accuracy and knowledge base evaluation of ChatGPT-4o, Gemini-2.0-Flash, and DeepSeek-V3 in metabolic and bariatric surgeryHany, Mohamed (Avtor) Zidan, Mohamed H. (Avtor) Parmar, Chetan (Avtor) Shahabi, Shahab (Avtor) Altabbaa, Hashem (Avtor) Pintar, Tadeja (Sodelavec pri raziskavi) large language modelsmetabolic and bariatric surgeryartificial intelligence evaluationChatGPTDeepSeek-V3GeminiBackground: Large language models (LLMs) are increasingly applied in medicine; however, their accuracy in guideline-driven, high-stakes specialties, such as metabolic and bariatric surgery (MBS), remains uncertain. This study evaluates the performance of ChatGPT-4o, Gemini 2.0 Flash, and DeepSeek-V3 in generating guideline-concordant responses to MBS clinical questions. Methods: Thirty standardized, guideline-based MBS questions were presented to each model. Responses were randomized in order, anonymized (blinded as Model A/B/C), and evaluated by 93 MBS experts using a validated 0–3 scale (0 = inaccurate; 3 = fully guideline-concordant). A repeated-measures ANOVA with Bonferroni correction tested model differences; reliability was assessed with Cronbach’s α and intraclass correlation coefficients (ICC). Results: DeepSeek-V3 achieved the highest mean score (2.44 ± 0.40), followed by ChatGPT-4o (1.79 ± 0.46) and Gemini 2.0 Flash (1.63 ± 0.47) (p < 0.001). Fully guideline-concordant ratings (score = 3) were most frequent for DeepSeek (80%) vs. ChatGPT (0%) and Gemini (3.3%). Internal consistency was excellent (α > 0.90), and inter-rater reliability was strong (ICC > 0.88). When mapped against the QUEST evaluation framework, the study addressed Quality and Understanding but did not fully capture Expression, Safety, or Trust dimensions. Conclusions: DeepSeek-V3 outperformed ChatGPT-4o and Gemini 2.0 Flash in generating guideline-concordant responses in MBS. These results highlight the need for ongoing, domain-focused validation before clinical use.20262026-07-01 14:02:52Neznano30747sl