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

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.

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Publication Details

Journal
JMIR AI
Published
2026-01-22
DOI
https://doi.org/10.2196/75064
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Ashenafi Zebene Woldaregay, Kjersti Mevik, Miguel Tejedor, Eva Lindell Jonsson et al.
JMIR AI
Artificial Intelligence in Healthcare and Education
article

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

Ashenafi Zebene Woldaregay, Kjersti Mevik, Miguel Tejedor, Eva Lindell Jonsson, Claire Temple-Oberle
article en

Abstract

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.

JMIR AIVol. 5
Uppsala University (SE), University Hospital of North Norway (NO), University of Calgary (CA), Norwegian University of Science and Technology (NO), Nordland Hospital Trust (NO), UiT The Arctic University of Norway (NO)
Helse Nord RHF
Openalex Percentile: Top 83%
Artificial Intelligence in Healthcare and Education
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Application of AI Models for Preventing Surgical Complications: Scoping Review of Clinical Readiness and Barriers to Implementation — Ashenafi Zebene Woldaregay, Kjersti Mevik, et al. · JMIR AI (2026) | TGRS Research Map | TGRS