Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care.

Background: Artificial Intelligence (AI) has emerged as a transformative tool in dermatology, particularly in Low- and Middle-Income Countries (LMICs), where healthcare systems face challenges such as a shortage of dermatologists and limited resources. AI technologies, including deep learning models like Convolutional Neural Networks (CNNs), have demonstrated potential in improving diagnostic accuracy for skin diseases, which contribute significantly to the global disease burden. However, most research has focused on High-Income Countries (HICs), leaving gaps in understanding AI's applicability and effectiveness in LMICs.

Aim/objective: This systematic review critically evaluates the application of AI in dermatological practice within LMICs, assessing the performance of AI technologies across diverse geographic regions.

Methodology: The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and included 19 studies from databases including PubMed, Embase, and Cochrane. Eligible studies evaluated AI applications in dermatology within LMICs, reporting metrics like sensitivity, specificity, precision, and accuracy. Data extraction and quality assessment were performed independently by several reviewers using tools like PROBAST and QUADAS-2. A qualitative synthesis as per SWiM guidelines was conducted due to heterogeneity in study designs and outcomes.

Conclusion: AI shows significant promise in enhancing dermatological diagnostics and expanding access to dermatologic care in LMICs, with models achieving high accuracy (up to 99%) in tasks like skin cancer and infectious disease detection. However, challenges such as underrepresented skin tones in datasets, limited clinical validation, and infrastructural barriers currently hinder equitable implementation. Future efforts should prioritize creating and utilizing diverse datasets, lightweight models for mobile deployment, and human-AI collaboration to ensure context-specific and scalable solutions. Addressing these gaps can help leverage AI to mitigate global health disparities in dermatological care.