| Title: |
Artificial Intelligence technologies for assessing skin lesions for referral on the urgent suspected cancer pathway to detect benign lesions and reduce secondary care specialist appointments:early value assessment |
| Authors: |
SIMMONDS, MARK CRAWFORD; WALTON, MATTHEW JAMES; Llewellyn, ALEXIS ROBERT; UPHOFF, NOORTJE; LORD, JOSEPH; Harden, Melissa; HODGSON, ROB |
| Publication Year: |
2026 |
| Collection: |
White Rose Research Online (Universities of Leeds, Sheffield & York) |
| Description: |
Background Skin cancers are some of the most common types of cancer. Dermatology services receive about 1.2 million referrals a year, but only a small minority are confirmed skin cancer. Artificial intelligence may be helpful in the diagnosis of skin cancer by identifying lesions that are or are not cancerous. Objectives To investigate the clinical and cost-effectiveness of two artificial intelligence technologies: DERM (Deep Ensemble for Recognition of Malignancy, Skin Analytics) and Moleanalyzer Pro (FotoFinder), as decision aids following a primary care referral. Methods A rapid systematic review of evidence on the two technologies was conducted. A narrative synthesis was performed, with a meta-analysis of diagnostic accuracy data. Published and unpublished cost-effectiveness evidence on the named technologies, as well as other diagnostic technologies were reviewed. A conceptual model was developed that could form the basis of a full economic evaluation. Results Four studies of DERM and two of Moleanalyzer Pro were subject to full synthesis. DERM had a sensitivity of 96.1% to detect any malignant lesion (95% confidence interval 95.4 to 96.8); at a specificity of 65.4% (95% confidence interval 64.7 to 66.1). For detecting benign lesions, the sensitivity was 71.5% (95% confidence interval 70.7 to 72.3) for a specificity of 86.2% (95% confidence interval 85.4 to 87.0). Moleanalyzer Pro had lower sensitivity, but higher specificity for detecting melanoma than face-to-face dermatologists. DERM might lead to around half of all patients being discharged without assessment by a dermatologist, but a small number of malignant lesions would be missed. Patient and clinical opinions showed substantial resistance to using artificial intelligence without any assessment of lesions by a dermatologist. No published assessments of the cost-effectiveness of the technologies were identified; three assessments related to skin cancer more broadly in a National Health Service setting were identified. These studies employed similar ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| ISSN: |
2046-4924 |
| Relation: |
https://eprints.whiterose.ac.uk/id/eprint/237646/1/3049943.pdf; SIMMONDS, MARK CRAWFORD orcid.org/0000-0002-1999-8515 , WALTON, MATTHEW JAMES orcid.org/0000-0003-1932-3689 , Llewellyn, ALEXIS ROBERT orcid.org/0000-0003-4569-5136 et al. (4 more authors) (2026) Artificial Intelligence technologies for assessing skin lesions for referral on the urgent suspected cancer pathway to detect benign lesions and reduce secondary care specialist appointments:early value assessment. Health technology assessment. 3049943. ISSN: 2046-4924 |
| DOI: |
10.3310/GJMS0317 |
| Availability: |
https://eprints.whiterose.ac.uk/id/eprint/237646/; https://eprints.whiterose.ac.uk/id/eprint/237646/1/3049943.pdf; https://doi.org/10.3310/GJMS0317 |
| Rights: |
cc_by |
| Accession Number: |
edsbas.F103BEA8 |
| Database: |
BASE |