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Lookup NU author(s): Sam Tingle, Dr Sofia Kazerouni, Professor Colin Wilson, Dr George KourounisORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/. Summary: Artificial intelligence (AI) has the potential to expand access to high-quality surgical care in low-income and middle-income countries (LMICs), yet the extent and maturity of AI research in these settings remain unclear. We conducted a prospectively registered scoping review (osf.io/9PV6A) to synthesize primary evidence on the use of AI in LMIC surgical care. PubMed, Scopus, and Web of Science were searched for studies evaluating AI in surgical contexts within LMICs up to July 14, 2025. From 2602 records, 475 studies met inclusion criteria. Most were conducted in upper-middle-income countries (n = 376, 79·1%), with the overwhelming majority from China (n = 305, 64·2%). Only 46 studies (9·7%) were conducted in lower-middle-income countries and 5 (1·1%) in low-income countries. Research was predominantly retrospective (68%), and only nine randomised controlled trials were identified (2%). Most studies focused on model development (67%), with few reporting external validation (30%) or clinical deployment (3%), mostly as pilot trial-based integrations. Barriers to AI implementation included fragmented data systems, limited infrastructure, and workforce constraints. Facilitators included widespread smartphone access and growing international collaborations. Despite rapid growth, AI research remains in the early stages of development. Focus on model accuracy alone is insufficient if health systems lack the capacity for adoption and integration.
Author(s): Valli AD, Tingle SJ, Kazerouni S, Raj Kalpana TS, Karki B, Knight SR, Wilson C, Kourounis G
Publication type: Review
Publication status: Published
Journal: eClinicalMedicine
Year: 2026
Volume: 94
Print publication date: 01/04/2026
Online publication date: 16/03/2026
Acceptance date: 02/03/2026
ISSN (electronic): 2589-5370
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.eclinm.2026.103836
DOI: 10.1016/j.eclinm.2026.103836
Data Access Statement: The extracted summary data supporting the findings of this review can be obtained from the corresponding author upon reasonable request and without restriction. All data will be made available from the date of publication.
