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. Results: Across five prespecified seeds, macro-F1 decreased from 74.27 ± 1.36% on the source domain to 54.92 ± 2.60% on PAD-UFES-20. Calibration, conformal efficiency, and selective coverage also deteriorated externally, although macro-AUROC remained comparatively preserved. The independent DDI audit showed limited OOD separation, while explanation stability was interpreted only as descriptive evidence. Ablation analysis indicated that clinical metadata contributed importantly to external performance. Conclusions: Internally reproducible discrimination did not guarantee reliable external behavior. These findings support explicit external reliability auditing and specialist-supervised selective decision support before cross-device deployment.