Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

Laura Awad; Samrhuddi Tagalpallewar; Sunil Parthiban; Ruiyan Jiao; Diya Shah; Harry Burton; Peter Em Butler; Allan Ponniah

doi:10.1016/j.bjps.2026.06.023

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Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

Laura Awad·Samrhuddi Tagalpallewar·Sunil Parthiban·Ruiyan Jiao·Diya Shah·Harry Burton·Peter Em Butler·Allan Ponniah

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Abstract

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

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Humans Artificial Intelligence Skin Neoplasms Dermoscopy Melanoma Basal Cell Carcinoma Sensitivity and Specificity Microscopy, Confocal Artificial intelligence Diagnosis Non-invasive Skin cancer Tools Systematic Review Journal Article Meta-Analysis Review

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Laura Awad, Samrhuddi Tagalpallewar, Sunil Parthiban, Ruiyan Jiao, Diya Shah, Harry Burton, Peter Em Butler, Allan Ponniah. 2026-06-26. Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.. https://doi.org/10.1016/j.bjps.2026.06.023

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