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An artificial intelligence based app for skin cancer detection evaluated in a population based setting
Artificial intelligence (AI) based algorithms for classification of suspicious skin lesions have been implemented in mobile phone apps (mHealth), but their effect on healthcare systems is undocumented. In 2019, a large Dutch health insurance company offered 2.2 million adults free access to an mHeal...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199884/ https://www.ncbi.nlm.nih.gov/pubmed/37210466 http://dx.doi.org/10.1038/s41746-023-00831-w |
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author | Smak Gregoor, Anna M. Sangers, Tobias E. Bakker, Lytske J. Hollestein, Loes Uyl – de Groot, Carin A. Nijsten, Tamar Wakkee, Marlies |
author_facet | Smak Gregoor, Anna M. Sangers, Tobias E. Bakker, Lytske J. Hollestein, Loes Uyl – de Groot, Carin A. Nijsten, Tamar Wakkee, Marlies |
author_sort | Smak Gregoor, Anna M. |
collection | PubMed |
description | Artificial intelligence (AI) based algorithms for classification of suspicious skin lesions have been implemented in mobile phone apps (mHealth), but their effect on healthcare systems is undocumented. In 2019, a large Dutch health insurance company offered 2.2 million adults free access to an mHealth app for skin cancer detection. To study the impact on dermatological healthcare consumption, we conducted a retrospective population-based pragmatic study. We matched 18,960 mHealth-users who completed at least one successful assessment with the app to 56,880 controls who did not use the app and calculated odds ratios (OR) to compare dermatological claims between both groups in the first year after granting free access. A short-term cost-effectiveness analysis was performed to determine the cost per additional detected (pre)malignancy. Here we report that mHealth-users had more claims for (pre)malignant skin lesions than controls (6.0% vs 4.6%, OR 1.3 (95% CI 1.2–1.4)) and also a more than threefold higher risk of claims for benign skin tumors and nevi (5.9% vs 1.7%, OR 3.7 (95% CI 3.4–4.1)). The costs of detecting one additional (pre)malignant skin lesion with the app compared to the current standard of care were €2567. Based on these results, AI in mHealth appears to have a positive impact on detecting more cutaneous (pre)malignancies, but this should be balanced against the for now stronger increase in care consumption for benign skin tumors and nevi. |
format | Online Article Text |
id | pubmed-10199884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101998842023-05-22 An artificial intelligence based app for skin cancer detection evaluated in a population based setting Smak Gregoor, Anna M. Sangers, Tobias E. Bakker, Lytske J. Hollestein, Loes Uyl – de Groot, Carin A. Nijsten, Tamar Wakkee, Marlies NPJ Digit Med Article Artificial intelligence (AI) based algorithms for classification of suspicious skin lesions have been implemented in mobile phone apps (mHealth), but their effect on healthcare systems is undocumented. In 2019, a large Dutch health insurance company offered 2.2 million adults free access to an mHealth app for skin cancer detection. To study the impact on dermatological healthcare consumption, we conducted a retrospective population-based pragmatic study. We matched 18,960 mHealth-users who completed at least one successful assessment with the app to 56,880 controls who did not use the app and calculated odds ratios (OR) to compare dermatological claims between both groups in the first year after granting free access. A short-term cost-effectiveness analysis was performed to determine the cost per additional detected (pre)malignancy. Here we report that mHealth-users had more claims for (pre)malignant skin lesions than controls (6.0% vs 4.6%, OR 1.3 (95% CI 1.2–1.4)) and also a more than threefold higher risk of claims for benign skin tumors and nevi (5.9% vs 1.7%, OR 3.7 (95% CI 3.4–4.1)). The costs of detecting one additional (pre)malignant skin lesion with the app compared to the current standard of care were €2567. Based on these results, AI in mHealth appears to have a positive impact on detecting more cutaneous (pre)malignancies, but this should be balanced against the for now stronger increase in care consumption for benign skin tumors and nevi. Nature Publishing Group UK 2023-05-20 /pmc/articles/PMC10199884/ /pubmed/37210466 http://dx.doi.org/10.1038/s41746-023-00831-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Smak Gregoor, Anna M. Sangers, Tobias E. Bakker, Lytske J. Hollestein, Loes Uyl – de Groot, Carin A. Nijsten, Tamar Wakkee, Marlies An artificial intelligence based app for skin cancer detection evaluated in a population based setting |
title | An artificial intelligence based app for skin cancer detection evaluated in a population based setting |
title_full | An artificial intelligence based app for skin cancer detection evaluated in a population based setting |
title_fullStr | An artificial intelligence based app for skin cancer detection evaluated in a population based setting |
title_full_unstemmed | An artificial intelligence based app for skin cancer detection evaluated in a population based setting |
title_short | An artificial intelligence based app for skin cancer detection evaluated in a population based setting |
title_sort | artificial intelligence based app for skin cancer detection evaluated in a population based setting |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199884/ https://www.ncbi.nlm.nih.gov/pubmed/37210466 http://dx.doi.org/10.1038/s41746-023-00831-w |
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