This systematic review and meta-analysis evaluates the impact of artificial intelligence (AI) assistance on the accuracy of skin cancer diagnosis by clinicians. The study searched multiple databases from January 1, 2017, to November 8, 2022, and included studies comparing the performance of clinicians with and without AI assistance. Ten studies were eligible for meta-analysis, involving a total of 67,700 diagnostic evaluations. The results showed that AI assistance improved diagnostic accuracy, with a pooled sensitivity of 81.1% (95% CI 74.4–86.5) and specificity of 86.1% (95% CI 79.2–90.9). Subgroup analyses revealed that AI assistance benefited all levels of clinical experience, with the largest improvement observed in non-dermatologists. The study highlights the potential of AI in enhancing diagnostic accuracy, particularly for less experienced clinicians, but emphasizes the need for further research in real-life clinical settings to validate these findings.This systematic review and meta-analysis evaluates the impact of artificial intelligence (AI) assistance on the accuracy of skin cancer diagnosis by clinicians. The study searched multiple databases from January 1, 2017, to November 8, 2022, and included studies comparing the performance of clinicians with and without AI assistance. Ten studies were eligible for meta-analysis, involving a total of 67,700 diagnostic evaluations. The results showed that AI assistance improved diagnostic accuracy, with a pooled sensitivity of 81.1% (95% CI 74.4–86.5) and specificity of 86.1% (95% CI 79.2–90.9). Subgroup analyses revealed that AI assistance benefited all levels of clinical experience, with the largest improvement observed in non-dermatologists. The study highlights the potential of AI in enhancing diagnostic accuracy, particularly for less experienced clinicians, but emphasizes the need for further research in real-life clinical settings to validate these findings.