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

Abstract

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.

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