# Artificial intelligence in surgical care within low-income and middle-income countries: a scoping review of development, validation, and deployment

## TL;DR

This paper reviews how AI is being used to improve surgical care in low- and middle-income countries, finding that most research is still in early stages and faces significant implementation challenges.

## Contribution

The study provides a comprehensive scoping review of AI in surgical care in low- and middle-income countries, highlighting gaps in research and deployment.

## Key findings

- Most AI research in surgical care is concentrated in upper-middle-income countries, particularly China.
- Only a small percentage of studies report external validation or clinical deployment of AI models.
- Barriers to AI adoption include poor data systems, limited infrastructure, and workforce constraints.

## Abstract

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.