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

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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Publication Details

Journal
British Journal of Dermatology
Published
2026-03-26
DOI
https://doi.org/10.1093/bjd/ljag120
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
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article

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

Luke Carson, R. J. Fleming, Colin Fleming, Emma Lennard et al.
British Journal of Dermatology
Cutaneous Melanoma Detection and Management
article

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

Luke Carson, R. J. Fleming, Colin Fleming, Emma Lennard, Rubeta N Matin
article en

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.

British Journal of Dermatology
Newcastle upon Tyne Hospitals NHS Foundation Trust (GB), Ninewells Hospital (GB), John Radcliffe Hospital (GB), University of Oxford (GB), Clinical Research Institute (US), British Association of Dermatologists (GB), Oxford University Hospitals NHS Trust (GB), Monash University (AU), University of Newcastle Australia (AU), Newcastle University (GB)
Good health and well-being
Openalex Percentile: Top 4%
Cutaneous Melanoma Detection and Management
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Real-world effectiveness of AI-assisted lesion triage on cancer waiting times — Luke Carson, R. J. Fleming, et al. · British Journal of Dermatology (2026) | TGRS Research Map | TGRS