Artificial Intelligence Use in Acne Diagnosis and Management—A Scoping Review

Artificial intelligence (AI) techniques can allow for early diagnosis and treatment of acne. Bias in AI model training remains, leading to various challenges in achieving health equity in clinical practice. We aim to assess and provide an updated overview of (1) the types of AI-based tools developed for acne, (2) the various applications of AI in acne diagnosis and management, (3) the performance of these tools, and (4) the current data reported on skin diversity in AI model training. Correction added on 27 December 2025, after first online publication: The preceding sentence has been corrected. We queried PubMed, Cochrane and Scopus databases using the terms: "acne", "artificial intelligence", "machine learning", "deep learning", "large language model", and "ChatGPT". 105 articles were included for analysis. Of the 105 research articles, 96.2% (N = 101) were focused on acne diagnosis only, 9.5% (N = 10) on acne management only, and 5.7% (N = 6) on both. Most manuscripts used image-based models, including deep learning (76.2%, N = 80), classical machine learning (9.5%, N = 10), and ensemble models (11.4%, N = 12). The ensemble models hold the highest mean accuracy (89.7%), followed by deep learning (88.5%), large language models (87.5%), and machine learning models (86.9%). Only 13% (N = 14) of studies reported data on patient skin color, while 4 of the 14 studies included a full spectrum of diverse skin tones. Correction added on 27 December 2025, after first online publication: The preceding sentence has been corrected. The application of AI algorithms in healthcare is rapidly emerging, providing significant support to providers. With ensemble models demonstrating superior performance, AI algorithm use in acne may offer a convenient method to consistently diagnose and manage patients remotely. Designing systematic guidelines that require a diverse representation of all skin colors may improve social justice in healthcare.