# Application of AI Models for Preventing Surgical Complications: Scoping Review of Clinical Readiness and Barriers to Implementation

## TL;DR

This review explores how AI models can help prevent surgical complications but finds limited real-world adoption due to usability and trust issues.

## Contribution

The study provides a scoping review of AI models for preventing surgical complications, highlighting clinical readiness and barriers to implementation.

## Key findings

- AI models showed high technical accuracy in predicting surgical complications.
- Only a few AI models are routinely adopted in clinical practice.
- Barriers include usability issues, workflow misalignment, and trust concerns.

## Abstract

The impact of surgical complications is substantial and multifaceted, affecting patients and their families, surgeons, and health care systems. Despite the remarkable progress in artificial intelligence (AI), there remains a notable gap in the prospective implementation of AI models in surgery that use real-time data to support decision-making and enable proactive intervention to reduce the risk of surgical complications.

This scoping review aims to assess and analyze the adoption and use of AI models for preventing surgical complications. Furthermore, this review aims to identify barriers and facilitators for implementation at the bedside.

Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we conducted a literature search using IEEE Xplore, Scopus, Web of Science, MEDLINE, ProQuest, PubMed, ABI, Embase, Epistemonikos, CINAHL, and Cochrane registries. The inclusion criteria included empirical, peer-reviewed studies published in English between January 2013 and January 2025, involving AI models for preventing surgical complications (surgical site infections, and heart and lung complications or stroke) in real-world settings. Exclusions included retrospective algorithm-only validations, nonempirical research (eg, editorials or protocols), and non-English studies. Study characteristics and AI model development details were extracted, along with performance statistics (eg, sensitivity and area under the receiver operating characteristic curve). We then used thematic analysis to synthesize findings related to AI models, prediction outputs, and validation methods. Studies were grouped into three main themes: (1) duration of hypotension, (2) risk for complications, and (3) decision support tool.

Of the 275 identified records, 19 were included. The included models frequently demonstrated strong technical accuracy with high sensitivity and area under the receiver operating characteristic curve, particularly among studies evaluating decision support tools. However, only a few models were adopted routinely in clinical practice. Two studies evaluated the clinicians’ perceptions regarding the use of AI models, reporting predominantly positive assessments of their usefulness.

Overall, AI models hold potential to predict and prevent surgical complications as the validation studies demonstrated high accuracy. However, implementation in routine practice remains limited by usability barriers, workflow misalignment, trust concerns, and financial and ethical constraints. The evidence included in this scoping review was limited by the heterogeneity in study design and the predominance of small-scale feasibility studies, particularly for hypotension prediction. Future research should prioritize prospectively validated models that use other physiologic features and address clinicians’ concerns regarding generalizability and adoption.