Artificial intelligence in acne vulgaris: Current applications, limitations, and future directions

Artificial intelligence (AI) has gained increasing attention in dermatology, particularly in acne vulgaris, a highly prevalent disorder in which visual pattern recognition and severity grading play a central role. AI-based systems have demonstrated encouraging performance in automated acne detection, lesion classification, and objective severity assessment using standardized grading scales, with potential to reduce inter-observer variability and improve reproducibility in both clinical and research settings. Recent advances in deep learning and Ensemble models have enabled lesion-level analysis and more consistent phenotyping. This narrative review synthesizes current evidence on the application of AI in acne vulgaris, based on a review of published English-language literature identified through major biomedical databases and manual reference screening. Beyond diagnosis and severity assessment, emerging applications include patient education platforms, over-the-counter product recommendation tools, adherence monitoring, and longitudinal evaluation through serial image analysis. Natural language processing and large language models may further support acne care by assisting with clinical documentation, tele dermatology triage, and personalized counseling. Despite these advances, translation into routine clinical practice remains limited. Key challenges include limited external and prospective validation, dataset bias, underrepresentation of skin of color, and a predominant focus on algorithmic accuracy without evidence of impact on clinical outcomes. Ethical concerns related to data privacy, transparency, and medicolegal responsibility also warrant attention. AI should be viewed as an adjunctive, decision-support tool rather than a substitute for clinical judgment. Dermatologist oversight remains essential for the responsible and equitable integration of AI into acne care.