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March 30, 2026British Journal of Dermatology0 citations

Real-world effectiveness of AI-assisted lesion triage on cancer waiting times

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LCLuke CarsonRFR. J. FlemingELEmma Lennard

Key Points

  • This research evaluates the effectiveness of AI-assisted lesion triage in reducing cancer waiting times.
  • Real-world interrupted time series analysis and meta-analysis conducted using data from 24 NHS trusts.
  • Assessment of Faster Diagnosis Standard breaches in patients referred with suspected skin cancer.
  • Comparison of waiting time data pre and post AI-assisted triage deployment.
  • No consistent pooled improvement in Faster Diagnosis Standard breaches was observed.
  • Marked heterogeneity in outcomes across different NHS trusts, with some showing benefit while others exhibited deterioration.
  • Findings underscore the need for local evaluations before widespread adoption of AI in dermatology services.

Abstract

Urgent skin cancer referrals are rising, and autonomous AI tools have been proposed as a solution to the significant pressure on dermatology services. Using publicly available cancer waiting time data from 24 NHS trusts, this real-world interrupted time series and meta-analysis showed no consistent pooled improvement in Faster Diagnosis Standard breaches (the proportion of patients referred with suspected cancer waiting over 28 days from referral to diagnosis) following deployment of an AI-assisted lesion triage system, with marked heterogeneity ranging from benefit to deterioration across trusts. These findings highlight that the impact of diagnostic AI is highly context-dependent and underscore the need for robust local evaluation and cost-benefit assessment before widespread adoption.

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Cite This Study

Carson et al. (2026) studied this question.

synapsesocial.com/papers/69c9c51bf8fdd13afe0bd0f5https://doi.org/10.1093/bjd/ljag120
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