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

Abstract

Abstract 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.