Artificial Intelligence in Dermatology in Sub-Saharan Africa: A Scoping Review Protocol

Background: Sub-Saharan Africa (SSA) faces a critical shortage of dermatology specialists, with fewer than 1 dermatologist per 1 million people in some countries. Skin diseases, including Neglected Tropical Diseases (NTDs), fungal infections, and skin cancers, cause substantial morbidity, yet diagnostic capacity remains severely limited. Artificial intelligence (AI) technologies offer potential solutions through image-based diagnosis, but implementation in resource-limited settings faces unique challenges. Objectives: This scoping review aims to map the current landscape of AI technologies applied to dermatology in SSA, identify implementation barriers and facilitators using the Consolidated Framework for Implementation Research (CFIR), and assess feasibility for deployment in resource-limited settings. Methods: We will include original research (diagnostic accuracy studies, implementation studies, qualitative research, mixed methods, case studies) and systematic/scoping reviews published between January 1, 2018, and March 31, 2026, in English or French. Studies must evaluate AI-based dermatological tools in SSA settings and report performance metrics or implementation outcomes. We will search PubMed, IEEE Xplore, Google Scholar, and African Journals Online (AJOL), complemented by grey literature from WHO, WHO AFRO, KEMRI, and clinical trial registries. Two independent reviewers will screen titles/abstracts and full texts using Rayyan. Data extraction will use a standardized form capturing study characteristics, AI technology, disease focus, performance metrics, and implementation determinants mapped to CFIR domains. Data will be synthesized narratively following Synthesis Without Meta-Analysis (SWiM) guidelines. Results: Findings will be presented as a narrative synthesis with descriptive mapping, CFIR-framed implementation analysis, feasibility assessment, and a practical implementation framework for AI dermatology deployment in SSA. Conclusions: This review will synthesize evidence on AI dermatology in SSA, identify implementation barriers and facilitators, and provide guidance for responsible AI deployment in resource-limited settings