Comparing Predictive Accuracy of Bleeding in Total Abdominal Hysterectomy Among Anesthesiologists, Gynecologists and AI: A Clinical Observational Study

Authors : Ali Selçuk Yeniocak, Can Tercan, Emrah Dağdeviren, Emrullah Akay, Osman Murat Güler, Alperen İnce, Necmiye Ay, Havva Betül Bacak, Enes Serhat Coşkun

Pages : 96-103

View : 19 | Download : 17

Publication Date : 2025-04-30

Article Type : Research Paper

Abstract :Aim: Accurate prediction of perioperative blood loss is critical for optimizing outcomes in total abdominal hysterectomy (TAH). Traditional estimation methods by clinicians are subjective and prone to variability, while artificial intelligence (AI) offers a potential data-driven alternative. This study compares the predictive accuracy of anesthesiologists, gynecologists, and the AI algorithm ChatGPT4.0 for blood loss during TAH. Material and Methods: This single-center, prospective observational study evaluated 50 patients who underwent TAH for benign conditions in 2023. Clinical data, including uterine size, surgical duration, and surgeon experience, were retrospectively collected. Participating gynecologists and anesthesiologists predicted intraoperative blood loss based on anonymized patient data. Predictions were compared to ChatGPT4.0’s outputs and actual recorded blood loss, categorized into mild, moderate, and severe bleeding levels. Sensitivity, positive predictive value, and overall accuracy were analyzed using statistical tests appropriate for data distribution. Results: Anesthesiologists achieved the highest overall accuracy (40%), excelling in moderate bleeding predictions. Gynecologists demonstrated moderate performance across all categories, with 38% accuracy. ChatGPT4.0 showed the lowest overall accuracy (34%) but outperformed clinicians in predicting severe bleeding (37.5% positive predictive value). Variability in clinician predictions highlighted the challenges of subjective estimation, while AI predictions demonstrated consistency but limited precision. Conclusions: AI offers promise in enhancing objective blood loss prediction, particularly for severe cases. However, its performance remains inferior to clinician estimates in most scenarios, underscoring the need for further algorithm refinement and integration into clinical workflows. Future research should focus on long-term validation and addressing ethical challenges in AI adoption.
Keywords : AI, anesthesiology, artificial intelligence, blood loss prediction, ChatGPT, gynecology, total abdominal hysterectomy