From algorithm performance to clinical value in AI-assisted skin cancer diagnostics
4 affiliations
- 1Department of Dermatology, Allergology and Venerology, University Hospital Essen, Bochum, Germany
- 2Department of Dermatology, Allergology and Venerology, Ruhr-University Bochum, Bochum, Germany
- 3Department of Dermatology, Bielefeld University, Medical School and University Medical Center OWL, Klinikum Bielefeld Rosenhöhe, Bielefeld, Germany
- 4International Institute of Management in Technology (IIMT), University of Fribourg, Fribourg, Switzerland
Abstract
Background: Artificial 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.
Methods: We 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.
Results: Of 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.
Discussion: Current 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.
Conclusion: Despite 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.
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BibTeX
@article{ockerserger2026from,
title = {{From algorithm performance to clinical value in AI-assisted skin cancer diagnostics}},
author = {Lennart Ocker-Serger and Elisabeth Livingstone and Georg Lodde and Eggert Stockfleth and Nessr Abu Rached and Dirk Schadendorf and Stephan Nüesch},
journal = {Frontiers in Digital Health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1896521},
url = {https://doi.org/10.3389/fdgth.2026.1896521},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From algorithm performance to clinical value in AI-assisted skin cancer diagnostics
AU - Lennart Ocker-Serger
AU - Elisabeth Livingstone
AU - Georg Lodde
AU - Eggert Stockfleth
AU - Nessr Abu Rached
AU - Dirk Schadendorf
AU - Stephan Nüesch
PY - 2026
DA - 2026/10/12
JO - Frontiers in Digital Health
DO - 10.3389/fdgth.2026.1896521
UR - https://doi.org/10.3389/fdgth.2026.1896521
AB - Background: Artificial 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. Methods: We 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. Results: Of 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. Discussion: Current 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. Conclusion: Despite 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.
SN - 2673-253X
ER -