Learning new surgical techniques in low and middle income countries, approval processes, and the impact of artificial intelligence - Duy Tran, Long Cong; Metwalli, Helal; Tien Le, Dat; Amo-Afful, Samuel; Todry Gerges, Mario Salib; Hassan Ahmed, Hajer Hatim; Kim Quyen, Dinh Thi; Gamil Gad, Abdelrahman; Tran, Phillip; Huy, Nguyen Tien (Frontiers in Surgery, 2025)
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.