Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review.

BACKGROUND: Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated potential in prediction tasks, yet its methodological rigor and clinical translation face challenges. OBJECTIVE: This scoping review aims to systematically synthesize the current state of development, performance, and validation of ML models for predicting intraoperative bleeding, and to identify key barriers to their clinical implementation. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched 7 databases (PubMed, Web of Science, Embase, CINAHL, CNKI [China National Knowledge Infrastructure], Wanfang, and VIP [China Science and Technology Journal Database]) from their inception to April 2025. Moreover, 2 reviewers (SY and PZ) independently screened studies, extracted data using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), and assessed the risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). A narrative synthesis was used for data analysis. RESULTS: Out of 2651 screened records, 23 studies were included (sample sizes ranging from 48 to 48,543). Tree-based ensemble models (eg, random forests and extreme gradient boosting) were the most frequently used (16/23, 70%), followed by logistic regression (13/23, 57%), and deep learning (11/23, 48%). Model discrimination varied widely (mean area under the curve [AUC] 0.82, SD 0.08, range 0.63-0.93). Integration of multimodal data (electronic health records+imaging) was associated with higher performance. However, model validation was often inadequate; only 6 studies (6/23, 26%) performed external validation, and performance often declined (eg, AUC decreased from 0.85 to 0.63 in 1 study). Reporting exhibited selective bias; AUC was commonly reported (19/23, 83%), whereas key classification metrics, such as calibration (10/23, 43%) and precision (4/23, 17%), were often omitted. PROBAST assessment indicated a high risk of bias in all included studies (23/23, 100%). CONCLUSIONS: While ML models demonstrate technical promise for predicting intraoperative bleeding, our PROBAST assessment revealed a universally high risk of bias across all included studies. This fundamental methodological limitation, coupled with a severe lack of external validation and poor transparency in reporting, severely constrains the current clinical reliability of these models. Future research must prioritize prospective multicenter validation, adherence to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines, and enhanced model interpretability to bridge the gap toward clinical utility.