Reliability-Audited Explainable AI for Multi-Class Skin-Lesion 2 Triage Under Cross-Device Domain Shift

Background/Objectives: Reliable skin-lesion AI requires evaluation beyond the development distribution. This study assessed whether a source-developed multi-class system preserves discrimination and reliability when transferred to an unseen smartphone-acquired domain.

Methods

A source-only multimodal four-class classifier was developed using HAM10000 and Derm7pt and evaluated on PAD-UFES-20 without target-domain adaptation. Reliability was assessed using class-specific calibration, uncertainty-based error detection, class-conditional conformal prediction, selective classification, demographic subgroup auditing, controlled ablation, and an independent DDI out-of-distribution (OOD) and explainability (XAI) audit.