Diagnostic accuracy of artificial intelligence dermatology apps compared to clinical evaluation in Indian patients with common skin conditions

Background: Artificial intelligence (AI)-based dermatology mobile applications are increasingly used for preliminary diagnosis of skin conditions. However, their diagnostic accuracy in populations with darker skin tones, such as those in India, remains poorly studied. This research aimed to assess and compare the accuracy of three AI-based dermatology apps against dermatologist consensus diagnoses in Indian patients presenting with common dermatoses. Methods: A prospective, cross-sectional study was conducted on 32 patients attending a dermatology outpatient clinic in Hyderabad, India. Each patient was clinically diagnosed by board-certified dermatologists and subsequently evaluated using three AI apps: Aysa, Skinner by Arboreal, and AI Dermatologist Skin Scanner. The diagnostic output from each app was compared to the dermatologist consensus. Accuracy was calculated for top diagnosis and top three diagnoses. Cohen’s kappa was used to assess agreement. Results: Aysa showed the highest top diagnosis accuracy (59.4%), followed by Skinner (53.1%) and AI Dermatologist (46.9%). Diagnostic agreement varied by condition, with higher accuracy observed for acne vulgaris and tinea corporis. Performance was poorest for pigmentary and inflammatory disorders such as vitiligo and psoriasis. Diagnostic accuracy declined with increasing Fitzpatrick skin type, indicating skin tone bias in the AI models. Cohen’s kappa indicated moderate agreement for Aysa (κ=0.43) and fair agreement for the other two apps. Conclusions: While AI dermatology apps show moderate accuracy for certain conditions, they remain inconsistent and biased toward lighter skin tones. These tools may serve as preliminary screening aids but cannot substitute clinical judgment. Enhanced training on diverse skin tones is necessary for equitable AI deployment.