Preferences for the use of Artificial Intelligence (AI) technologies to help detect skin cancer in primary care settings: a UK-wide discrete choice experiment (DCE).

Owain T Jones, Fiona M Walter, Rubeta N Matin, Natalia Calanzani, Jon Emery, M van der Schaar, Stephen Morris

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Article in British journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Owain T JonesPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. ot.jones@bristol.ac.uk.ORCID http://orcid.org/0000-0003-2783-9431

Fiona M WalterPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Rubeta N MatinDermatology Department, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Natalia CalanzaniAcademic Primary Care, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.

Jon EmeryWolfson Institute of Population Health, Queen Mary University of London, London, UK.

M van der SchaarDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Stephen MorrisPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-5828-3563

Funding

Department of Health | National Health and Medical Research Council (NHMRC) APP1195302

6 · The paper itself

Abstract

backgroundAI technologies could help triage suspicious skin lesions. The aim of this study was to examine preferences of general practitioners (GPs), patients, and members of the public for different attributes of AI technologies to help detect skin cancer.

methodsA DCE using online surveys, with choice scenarios based on: false negative and positive rates, cost, location of AI technology, efficacy on different skin tones, and guideline recommendations. Data were analysed using alternative-specific conditional logit regression models.

results2302 participants completed the survey. All attributes significantly influenced the respondents' preferences. Ranking of attributes by relative importance was the same for GPs, patients, and members of the public: false negative rate was the most important attribute, followed by skin tones on which the technology had been developed and tested. False negative results had a significantly greater negative effect on GP choices than for other groups. Participants preferred AI technologies used by healthcare professionals in a GP surgery over AI technologies patients use at home.

conclusionsImplementation of AI technologies to detect skin cancer should focus on minimising false negative rates. Development and testing of AI technologies using data across all skin tones is essential. Preferences for AI technologies could have significant implications for successful implementation.

Identifiers

PMID42728369

DOI10.1016/j.eclinm.2023.102019

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