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How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.

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Artificial intelligence (AI) for skin cancer detection shows variable real-world reliability. Current evidence does not support unsupervised clinical deployment due to performance inconsistencies and persistent equity gaps in AI tools.

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Health Informatics

Background:

  • AI algorithms show promise for skin cancer detection in controlled settings.
  • Real-world reliability, performance across diverse populations, and clinical deployment readiness of AI remain uncertain.
  • This umbrella review synthesizes evidence to address these uncertainties.

Purpose of the Study:

  • To characterize AI performance across the skin cancer screening pathway.
  • To identify equity gaps in AI for skin cancer detection.
  • To assess the readiness of AI for clinical implementation.

Main Methods:

  • Umbrella review of systematic reviews and meta-analyses on AI for skin cancer detection.
  • Searched PubMed, Web of Science, CINAHL up to November 6, 2024.
  • Assessed study quality using ROBIS and synthesized findings narratively by screening phase.

Main Results:

  • 37 reviews (2008-2024) met inclusion criteria; most had high risk of bias.
  • Self-screening AI showed high performance variability (sensitivity 0-98%); specialist AI achieved dermatologist-level accuracy.
  • AI augmentation improved clinician sensitivity, particularly for generalists; engagement with skin tone/ethnicity was superficial; datasets favored light skin tones.

Conclusions:

  • Current evidence does not support unsupervised clinical deployment of AI-based skin cancer detection.
  • Self-screening tools exhibit inconsistent performance, and equity gaps persist.
  • Non-melanoma skin cancers are understudied, necessitating stage-specific validation and reporting standards.