Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses

作者：Nyi Nyi Soe, Ingsun Isika Kusnandar, Phyu Mon Latt, Christopher K. Fairley, Eric P.F. Chow, Ismaël Maatouk, Cheryl Johnson, Purvi Shah, Remco P. H. Peters, Lorenzo Subissi, Lei Zhang, Jason J. Ong · 发表于：JAMA Network Open · 年份：2025 · DOI：10.1001/jamanetworkopen.2025.33512 · 被引用次数：9 · 研究领域：Cutaneous Melanoma Detection and Management、AI in cancer detection、COVID-19 diagnosis using AI

Importance: Artificial intelligence (AI) excels in dermatology. However, its applications to sexually transmitted infections (STIs) remain unclear. Objective: To assess the performance of AI algorithms and their applications in detecting STIs and anogenital dermatoses from clinical images in sexual health. Data Sources: Six databases (IEEE Xplore, Embase, Scopus, Medline, Web of Science, and CINAHL) were searched for studies published from January 1, 2010, to April 12, 2024, using 3 main concepts: artificial intelligence, diagnosis, and sexually transmitted infections. Study Selection: Studies that used AI to identify anogenital skin conditions from clinical images were included. Studies that used non-AI approaches or nonanogenital conditions, as well as reviews and studies lacking performance metrics, were excluded. Data Extraction and Synthesis: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 2 reviewers independently assessed full-text articles and extracted data using a standardized spreadsheet. Another 2 reviewers resolved any disagreements. A modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) critical appraisal tool and the Checklist for Evaluation of Image-Based AI Reports in Dermatology (CLEAR Derm) were used for quality assessment. Main Outcomes and Measures: Pooled sensitivity and specificity of AI applications for detecting anogenital skin conditions. A bivariate random-effects meta-analysis w...