Table 2.

Comprehensive summary of findings with GRADE certainty: AI in Dermatological Diagnosis

Outcome	No. of studies	Total participants/Images	Effect estimate/Key results	Comments
Early detection of malignant skin lesions	5 studies [23, 27, 28, 35, 37]	~ 60,000 + images	Accuracy: 85–97% Sensitivity: 82–95%	Strong performance in model testing; external validation is still limited
Detection of monkeypox from skin images	5 studies [21, 24, 29, 32, 36]	~ 30,000 + images	Accuracy: 88–98%. Used SVMs, transformers, and few-shot learning	Models are promising but mostly based on small or synthetic datasets
Diagnostic accuracy of AI models for general dermatology	17 studies [21–29, 31–38]	~ 150,000 + images	Accuracy: 80–98% Sensitivity: 76–96% Specificity: 78–97%	High variability in algorithms and lesion types; many use public data sets
AI vs Dermatologist diagnostic performance	1 study [30]	~ 1,000 image assessments	Comparable grading accuracy for facial signs	Only one validation study; limited scope
Generalizability across populations and skin tones	3 studies [30, 31, 34]	Multinational(South Africa, China, Uganda)	Performance gaps noted in underrepresented groups	Highlights a lack of training data diversity
Explainability and transparency of AI predictions	5 studies [23, 27, 28, 31, 35]	Not quantifiable	Used tools like SHAP, Grad-CAM	Explainability present but limited validation and integration
Real-time or low-resource deployment feasibility	2 studies [26, 31]	Not specified	Use of lightweight/efficient models for mobile platforms	Mostly feasibility-focused; real-world performance unknown