Diagnostic accuracy of machine-learning–based image analysis for early detection of Kaposi sarcoma in people living with HIV

Background Kaposi sarcoma (KS) remains one of the most common HIV-associated malignancies in sub-Saharan Africa, where diagnostic delays contribute to advanced disease and poor outcomes. Machine-learning (ML)–based image analysis has emerged as a potential tool to support early KS detection, particularly in resource-limited HIV care settings. Aim To critically synthesise evidence on the reported diagnostic performance, imaging modalities, methodological considerations and clinical implications of ML-based image analysis for early detection of KS in people living with HIV. Methods A narrative review with structured literature synthesis was conducted. PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore were searched for publications from 2010 to 2026. Eligible studies evaluated machine-learning or artificial-intelligence models applied to medical images for Kaposi sarcoma detection or closely related methodological applications and reported relevant diagnostic or imaging outcomes. Data were extracted on study characteristics, imaging modality, ML approach, reference standard, diagnostic performance, validation strategy and methodological limitations, and findings were synthesised narratively across predefined themes. Results The principal KS-specific quantitative study reported a sensitivity of 89% (95% CI: 85–94%) and specificity of 51% (95% CI: 40–61%). These represent study-level estimates and were not pooled because of the limited number and substantial heterogeneity of KS-specific studies. Diagnostic performance varied according to imaging modality, dataset size and validation approach. Dermoscopic and histopathological ML applications generally demonstrated stronger reported performance, whereas photographic approaches offered greater potential for decentralised HIV care. Important methodological limitations included limited external validation, small datasets and under-representation of darker skin tones. Conclusion Current evidence suggests that ML-based image analysis may have potential as a decision-support tool for early KS detection, particularly for screening, triage and referral support in settings where specialist dermatological expertise is limited. However, the available KS-specific evidence remains preliminary, with substantial methodological heterogeneity, limited external validation and insufficient evidence for pooled estimates of diagnostic performance. Prospective, externally validated and equity-focused studies are required before routine clinical implementation can be recommended.