Comparison of clinical hysterectomy indications with ai-based recommendations: a prospective study

This prospective study evaluated ChatGPT-4 as a decision-support tool by comparing its treatment recommendations with clinical decisions for 87 women (aged 40-65 years) scheduled for hysterectomy. Demographic, clinical, and ultrasonographic data were standardized and submitted to the GPT-4 model, which generated evidence-based treatment suggestions. ChatGPT-4 recommended hysterectomy in 70.1% of cases, aligning with the original clinical decision, and suggested alternatives such as myomectomy (10.3%), hysteroscopy (8.0%), or medical therapy (4.6%) in others. These alternative options were retrospectively judged as appropriate in selected scenarios. Although the model demonstrated guideline-consistent reasoning, it lacked access to imaging, laboratory results, and physical examination findings. The study's single-center design, absence of sample size calculation, and purely descriptive nature further limit generalizability. Large language models may complement clinical decision-making but should not replace physician expertise. Multicenter studies are needed to validate their reliability and clinical applicability.