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.
Authors
- Ashenafi Zebene Woldaregay (ORCID: https://orcid.org/0000-0002-9464-3407)
- Kjersti Mevik (ORCID: https://orcid.org/0000-0002-0692-7240)
- Miguel Tejedor
- Eva Lindell Jonsson
- Claire Temple-Oberle
Institutions
- 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)
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
Funders
- Helse Nord RHF