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Войти в исследования British journal of cancer · 2026
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).
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Jones Owain T, Walter Fiona M, Matin Rubeta N, Calanzani Natalia, Emery Jon, van der Schaar M, Morris Stephen
Аннотация
BACKGROUND: AI 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.
METHODS: A 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.
RESULTS: 2302 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.
CONCLUSIONS: Implementation 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.
METHODS: A 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.
RESULTS: 2302 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.
CONCLUSIONS: Implementation 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.