What question did this study set out to answer?

The aim is to evaluate the effectiveness of AI tools in diagnosing benign skin lesions through sensitivity and specificity analysis.

April 19, 2026Skin Health and DiseaseOpen Access

How good are artificial intelligence tools at identifying benign skin lesions? A systematic review and meta-analysis of the specificity of artificial intelligence tools in diagnosing suspicious skin lesions

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A systematic review evaluates AI tools' effectiveness in diagnosing benign skin lesions, suggesting potential for improved diagnostics.

The aim is to evaluate the effectiveness of AI tools in diagnosing benign skin lesions through sensitivity and specificity analysis.
Conducted a comprehensive search on multiple databases following PRISMA 2020 guidelines.
Included studies with recorded sensitivity and specificity metrics.
Assessed nine studies meeting inclusion criteria.
Extracted data on AI model type, sample size, and diagnostic metrics.
Assessed risk of bias using QUADAS-2 framework.
AI tools showed specificity ranging from 70% to 95%, with a pooled specificity of 82%.
Sensitivity ranged from 65% to 96%, with a pooled sensitivity of 89%.
AI tools performed well in distinguishing between malignant and benign skin lesions.

Nirula et al. (2026) studied this question.

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April 19, 2026Skin Health and DiseaseOpen Access

View Full Paper

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Authors

A systematic review evaluates AI tools' effectiveness in diagnosing benign skin lesions, suggesting potential for improved diagnostics.

The aim is to evaluate the effectiveness of AI tools in diagnosing benign skin lesions through sensitivity and specificity analysis.
Conducted a comprehensive search on multiple databases following PRISMA 2020 guidelines.
Included studies with recorded sensitivity and specificity metrics.
Assessed nine studies meeting inclusion criteria.
Extracted data on AI model type, sample size, and diagnostic metrics.
Assessed risk of bias using QUADAS-2 framework.
AI tools showed specificity ranging from 70% to 95%, with a pooled specificity of 82%.
Sensitivity ranged from 65% to 96%, with a pooled sensitivity of 89%.
AI tools performed well in distinguishing between malignant and benign skin lesions.

Nirula et al. (2026) studied this question.

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