Training in surgery and approval of new techniques in low- and middle-income countries (LMICs), usually depends on informal apprenticeship systems, that is often lacking standardization, structured mentorship and produce inconsistent patient outcomes. These challenges are particularly severe in rural areas, where training opportunities and healthcare infrastructure are limited. Recently, artificial intelligence (AI) has emerged as a reliable solution, providing applicable, Quantitative methods for skill development, competency evaluation and regulatory supervision. AI-powered tools, such as virtual reality (VR) simulations and tele-mentoring platforms, provide independent skill assessments and expand access to high-quality surgical education. However, implementing AI in LMICs faces some challenges, including inadequate resources, financial constraints and ethical issues related to data security and Equitable algorithms. This review compares usual surgical training and approval processes in LMICs and evaluates the promising role of AI to fill existing gaps and compares both approaches in terms of applicability, cost-effectiveness and impact on patient outcomes.

Introduction

Implementing new surgical techniques, in Low and Middle -Income Countries (LMICs), often follows informal, apprenticeship-style pathways. Training usually runs through bedside observation, short-term workshops, or mentorship from visiting specialists. These approaches lack standardized curricula and objective assessment tools. This system can lead to wide variability in surgical competency among surgeons and patient outcomes, especially in rural areas where access to mentorship and infrastructure is limited (1). Despite the absence of strong, evidence-based validation, the successful application of a novel technique by a single experienced surgeon can be sufficient to gain institutional or even ministry-level approval for broader use (2). Recently, artificial intelligence (AI) has begun to reshape rules for surgical education in LMICs. AI-powered tools, particularly those using computer vision and machine learning, offer data-driven solutions for skill acquisition and performance assessment. These technologies can analyze surgical videos and kinematic data, to distinguish between expert and limited performance, providing standardized and reliable assessments (3, 4). AI platforms also enable personalized feedback and remote simulation-based learning, allowing trainees to practice and improve skills outside traditional settings (5). These new technologies are impactful in LMICs, where training opportunities are sparse and surgical capacity building is a critical need (1)

This review explores the traditional processes by which new surgical techniques are learned and approved in LMICs, examines how AI addresses existing limitations and compares these pathways to highlight their effects on surgical quality, information access and patient safety.

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