Responsible Artificial Intelligence Integration in Medical Student Education in Somalia and Low-Resource Settings: A Context-Sensitive Implementation Framework
TL;DR
This commentary proposes a context-sensitive implementation framework for responsible AI integration in medical student education in Somalia and comparable low-resource settings that prioritizes faculty oversight, academic integrity, patient confidentiality, multilingual verification, local clinical validation, low-bandwidth access, and outcome monitoring.
Abstract
Abstract Artificial intelligence (AI), particularly generative AI and large language models, is increasingly used by health-professions learners for explanation, revision, simulation, feedback, and assessment support, while institutional governance has not always kept pace. Somalia is a distinctive setting in which recent mapping identified 112 health-professions schools, many concentrated in urban areas, while medical education continues to face unequal access to faculty, simulation, standardized assessment, and reliable digital infrastructure. This commentary proposes a context-sensitive implementation framework for responsible AI integration in medical student education in Somalia and comparable low-resource settings. The framework was developed through a focused narrative synthesis of Somalia-specific educational evidence, peer-reviewed AI-in-medical-education literature, and international guidance from WHO, UNESCO, and the Association of American Medical Colleges. Its contribution is to translate broad AI principles into a resource-constrained implementation pathway that links Somalia-specific educational constraints to defined AI-supported functions, governance safeguards, phased institutional actions, and measurable educational outcomes. Published studies support the feasibility of AI-assisted feedback, virtual patient simulation, and tutoring, but also demonstrate variable accuracy and the need for human verification. The proposed model therefore prioritizes faculty oversight, academic integrity, patient confidentiality, multilingual verification, local clinical validation, low-bandwidth access, and outcome monitoring. A phased roadmap moves from policy and low-risk pilots to curriculum integration and multi-institutional evaluation. The framework is conceptual and requires prospective validation. If implemented with simultaneous investment in faculty development and assessment reform, AI may expand access to supervised practice without replacing teachers, patients, bedside learning, or professional judgment.