From algorithm performance to clinical value in AI-assisted skin cancer diagnostics

BackgroundArtificial intelligence (AI) has demonstrated high diagnostic performance in skin cancer detection and is increasingly being integrated into clinical workflows. However, whether these advances translate into meaningful patient-relevant and healthcare system outcomes remains unclear. Evidence on value-oriented outcomes has not been systematically synthesized.MethodsWe conducted a PRISMA-ScR–guided scoping review of studies published between January 2015 and October 2025 identified through PubMed/MEDLINE, Scopus, and Web of Science. Studies evaluating AI-based technologies for skin cancer diagnosis, triage, risk stratification, or management were included if they reported outcomes beyond technical performance metrics. Reported outcomes were mapped to three value-oriented domains: clinical and prognostic, process and experiential, and system-level and economic outcomes.ResultsOf 4,021 records identified, 33 studies met the inclusion criteria, whereas 389 publications were excluded because they reported only technical performance metrics without value-oriented outcomes. Reported outcomes were predominantly process and experiential (28/33 studies), including diagnostic confidence, workflow efficiency, and user acceptance. Clinical and prognostic outcomes were reported in only five studies, with four studies focussing on prognostic utility and one study focussing on patient management, while system-level and economic outcomes were identified in three studies. Most evaluations were conducted in controlled experimental settings, whereas prospective real-world implementation studies were less common. Overall, the available evidence predominantly focused on process-level benefits, with limited evidence for clinical and prognostic or system-level and economic outcomes.DiscussionCurrent evaluation strategies for AI-based skin cancer diagnostics remain predominantly performance-oriented and are largely focused on process and experiential outcomes. From a value-based healthcare perspective, current evidence remains largely confined to intermediate stages of the diagnostic value chain: although AI-assisted systems may improve diagnostic workflows, clinician confidence, and clinical decision making, few studies have evaluated their impact on downstream patient-relevant clinical and prognostic, or system-level and economic outcomes. Future research should prioritize prospective real-world studies, outcome-driven evaluation frameworks, and economic assessments that examine the impact of AI-supported decision-making on clinical outcomes and healthcare systems.ConclusionDespite promising effects on diagnostic processes, evidence for the clinical and system-level value of AI-based skin cancer diagnostics remains limited. Outcome-driven evaluation frameworks are needed to bridge the gap between algorithmic performance and meaningful healthcare impact.