This systematic review and meta-analysis evaluated the impact of AI assistance on the diagnostic accuracy of skin cancer in clinical settings. The study analyzed 2983 studies, of which 10 were included in the meta-analysis. The results showed that AI-assisted clinicians had higher sensitivity (81.1%) and specificity (86.1%) compared to clinicians without AI assistance (74.8% and 81.5%, respectively). AI assistance improved diagnostic accuracy across all experience levels, with the most significant improvements observed in non-dermatologists. No publication bias was detected, and the findings were robust after sensitivity analysis. The study highlights the potential of AI to enhance diagnostic accuracy in skin cancer diagnosis, particularly for less experienced clinicians. However, most studies were conducted in experimental settings, and further research is needed to evaluate the effectiveness of AI in real-world clinical environments. The study also emphasizes the importance of accurate and reliable AI algorithms, as well as the need for better transparency in AI tool reporting. Additionally, the study notes that AI-human collaboration could improve diagnostic performance by overcoming the limitations of either AI or human clinicians alone. The findings suggest that AI assistance may be especially beneficial for non-dermatologists and could become a standard part of dermatologists' toolkits in the future. The study also discusses the importance of considering factors such as human trust, confidence, and cognitive biases in AI-assisted decision-making. Overall, the study supports the notion that AI assistance can positively impact clinician diagnostic performance in skin cancer diagnosis.This systematic review and meta-analysis evaluated the impact of AI assistance on the diagnostic accuracy of skin cancer in clinical settings. The study analyzed 2983 studies, of which 10 were included in the meta-analysis. The results showed that AI-assisted clinicians had higher sensitivity (81.1%) and specificity (86.1%) compared to clinicians without AI assistance (74.8% and 81.5%, respectively). AI assistance improved diagnostic accuracy across all experience levels, with the most significant improvements observed in non-dermatologists. No publication bias was detected, and the findings were robust after sensitivity analysis. The study highlights the potential of AI to enhance diagnostic accuracy in skin cancer diagnosis, particularly for less experienced clinicians. However, most studies were conducted in experimental settings, and further research is needed to evaluate the effectiveness of AI in real-world clinical environments. The study also emphasizes the importance of accurate and reliable AI algorithms, as well as the need for better transparency in AI tool reporting. Additionally, the study notes that AI-human collaboration could improve diagnostic performance by overcoming the limitations of either AI or human clinicians alone. The findings suggest that AI assistance may be especially beneficial for non-dermatologists and could become a standard part of dermatologists' toolkits in the future. The study also discusses the importance of considering factors such as human trust, confidence, and cognitive biases in AI-assisted decision-making. Overall, the study supports the notion that AI assistance can positively impact clinician diagnostic performance in skin cancer diagnosis.