Supplementary Material for: The Role of Artificial Intelligence in Gynecologic Oncology Decision Making: A Feasibility Study

Objective: To examine the potential of artificial intelligence (AI) in gynecologic oncology decision making.
Design: Feasibility study.
Setting: Fictive.
Participants: Fictitious case vignettes of gynecologic carcinomas.
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 Chat-GPT 4.0, Google Gemini, and Bing-Copilot. Treatment approval decisions were based on standardized clinical and laboratory criteria.
Results: Two cases of breast cancer, one case of ovarian cancer, one case of cervical cancer and one case of endometrial cancer were evaluated. All three language models were able to evaluate all clinical cases and make therapy-relevant suggestions, with Chat-GPT providing the most clear and concise recommendations that were in three cases totally consistent with physician assessments.
Conclusions: The study demonstrates that AI models, such as Chat-GPT, 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.
Limitations: Feasibility study with five fictitious case vignettes.

研究目标：探讨人工智能（Artificial Intelligence，AI）在妇科肿瘤诊疗决策制定中的应用潜力。
研究设计：可行性研究。
研究场景：虚构场景。
研究对象：妇科恶性肿瘤的虚构病例 vignettes（case vignettes）。
研究方法：构建妇科恶性肿瘤虚构病例 vignettes（case vignettes），由不同专业经验层级的医师以及包括Chat-GPT 4.0、Google Gemini、Bing-Copilot在内的大语言模型进行评估；治疗审批决策基于标准化的临床与实验室检验标准。
研究结果：共评估2例乳腺癌、1例卵巢癌、1例宫颈癌及1例子宫内膜癌病例。三款大语言模型均可完成所有临床病例的评估并给出治疗相关建议，其中Chat-GPT的建议最为清晰简洁，在3例病例中与医师评估结果完全一致。
研究结论：本研究表明，Chat-GPT等AI模型可在一定程度上完成临床病例评估、识别临床及/或实验室异常指标，并给出治疗相关建议。尽管整体一致性较高，但在更为复杂的病例中仍存在差异，因此仍需人工解读。本研究结果凸显了AI在提升诊疗清晰度、缩短诊疗耗时及控制医疗成本方面的优势。未来研究可进一步探索AI在真实患者数据中的应用，并开发混合决策模型以优化其在临床实践中的整合应用。
研究局限性：本研究为仅包含5个虚构病例 vignettes（case vignettes）的可行性研究。