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Journals / Kafkas Journal of Medical Sciences / 2026 / Cilt: 15 Sayı: 1

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

Ali Selçuk Yeniocak(Department of Obstetrics and Gynecology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Can Tercan(Department of Obstetrics and Gynecology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Emrah Dağdeviren(Department of Obstetrics and Gynecology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Emrullah Akay(Department of Obstetrics and Gynecology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Osman Murat Güler(Department of Obstetrics and Gynecology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Alperen İnce(Department of Obstetrics and Gynecology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Necmiye Ay(Department of Anesthesiology, Basaksehir Cam and Sakura City Hospital, İstanbul, Türkiye)
Havva Betül Bacak(Department of Obstetrics and Gynecology, Gaziosmanpaşa Taksim Education and Research Hospital, İstanbul, Türkiye)
Enes Serhat Coşkun(Department of Obstetrics and Gynecology, Simav Doç. Dr. İsmail Karakuyu City Hospital, Kutahya, Türkiye)
Pages
96–103
Publication Date
2025-04-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Sensitivity, positive predictive value, and overall accuracy were analyzed using statistical tests appropriate for data distribution.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Accurate prediction of perioperative blood loss is critical for optimizing outcomes in total abdominal hysterectomy (TAH).
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Key Findings & Data:This study compares the predictive accuracy of anesthesiologists, gynecologists, and the AI algorithm ChatGPT4.0 for blood loss during TAH.
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Conclusion & Impact:Sensitivity, positive predictive value, and overall accuracy were analyzed using statistical tests appropriate for data distribution.
🔬Design:Araştırma Makalesi
📍Scope:2023 Verileri
⚙️Variables:anesthesiology · artificial intelligence · blood loss prediction

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.

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Cite This Article

Yeniocak, A. S., Tercan, C., Dağdeviren, E., Akay, E., Güler, O. M., İnce, A., Ay, N., Bacak, H. B., Coşkun, E. S. (2025). Comparing Predictive Accuracy of Bleeding in Total Abdominal Hysterectomy Among Anesthesiologists, Gynecologists and AI: A Clinical Observational Study. Kafkas Journal of Medical Sciences, Cilt: 15 Sayı: 1, 96–103. https://ajindex.com/articles/1631565

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