TY - JOUR
T1 - An artificial intelligence based app for skin cancer detection evaluated in a population based setting
AU - Smak Gregoor, Anna M.
AU - Sangers, Tobias E.
AU - Bakker, Lytske J.
AU - Hollestein, Loes
AU - Uyl – de Groot, Carin A.
AU - Nijsten, Tamar
AU - Wakkee, Marlies
N1 - Funding Information: This study was initiated by the Department of Dermatology from the Erasmus MC Cancer Institute and was funded with an unrestricted research grant from SkinVision B.V. In addition, the Department of Dermatology from the Erasmus MC Cancer Institute received funding from CZ Groep for the data linkage via a trusted third party. Funding Information: This study was initiated by the Department of Dermatology from the Erasmus MC Cancer Institute and was funded with an unrestricted research grant from SkinVision B.V. In addition, the Department of Dermatology from the Erasmus MC Cancer Institute received funding from CZ Groep for the data linkage via a trusted third party. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85160009691&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41746-023-00831-w
DO - https://doi.org/10.1038/s41746-023-00831-w
M3 - Article
C2 - 37210466
SN - 2398-6352
VL - 6
JO - NPJ digital medicine
JF - NPJ digital medicine
IS - 1
M1 - 90
ER -