Endpoint-aligned artificial intelligence for biopsy-sparing assessment of suspected basal cell carcinoma

Basal cell carcinoma (BCC) care follows a sequence of decisions from triage to pathological subtyping and depth assessment, and the information available changes at each step. To date, no artificial-intelligence (AI) tool using non-invasive inputs has been developed to support this full decision-making sequence. In this study, we developed a multi-endpoint AI framework matching non-invasive inputs to each decision point in 1,459 internal and 995 external patients. Triage macro-AUROC was 0.995 internally, 0.978 externally and 0.853 in a geographically distinct cohort, with risk stratification 0.943 and 0.899. For thickness, the highest-precision configuration used dermoscopy alone rather than all modalities (0.949 versus 0.881). Performance exceeded the 19-dermatologist mean on matched cases for every prespecified primary metric (all P [≤] 0.014). Local adaptation raised in-scope accuracy from 0.790 to 0.954 but shifted action-proxy routing toward the no-further-assessment classes for out-of-scope inputs, reducing sensitivity from 0.953 to 0.697. A validation-locked Mahalanobis gate enriched sensitivity among accepted cases to 0.775 at 0.791 coverage but only partially mitigated residual out-of-scope routing errors. These findings separate closed-set performance from scope control and support endpoint-specific validation of biopsy-sparing AI for BCC diagnosis and personalized treatment planning.