Digital Natives' Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study.

acceptance artificial intelligence concerns diagnostics digital natives online survey preferences skin cancer skin cancer screening

Journal

JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439

Informations de publication

Date de publication:
27 08 2021
Historique:
received: 27 07 2020
accepted: 13 04 2021
revised: 02 10 2020
entrez: 27 8 2021
pubmed: 28 8 2021
medline: 3 11 2021
Statut: epublish

Résumé

Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. We recruited participants below 35 years of age using three social media channels-Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants' attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants' preferences. We evaluated potential concerns using maximum difference scaling. We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants' demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

Sections du résumé

BACKGROUND
Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants.
OBJECTIVE
We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups.
METHODS
We recruited participants below 35 years of age using three social media channels-Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants' attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants' preferences. We evaluated potential concerns using maximum difference scaling.
RESULTS
We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use.
CONCLUSIONS
The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants' demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

Identifiants

pubmed: 34448722
pii: v9i8e22909
doi: 10.2196/22909
pmc: PMC8433862
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e22909

Informations de copyright

©Sarah Haggenmüller, Eva Krieghoff-Henning, Tanja Jutzi, Nicole Trapp, Lennard Kiehl, Jochen Sven Utikal, Sascha Fabian, Titus Josef Brinker. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 27.08.2021.

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Auteurs

Sarah Haggenmüller (S)

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Eva Krieghoff-Henning (E)

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Tanja Jutzi (T)

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Nicole Trapp (N)

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Lennard Kiehl (L)

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

Jochen Sven Utikal (JS)

Department of Dermatology, Heidelberg University, Mannheim, Germany.
Skin Cancer Unit, German Cancer Research Center, Heidelberg, Germany.

Sascha Fabian (S)

Department of Economics, University of Applied Science Neu-Ulm, Neu-Ulm, Germany.

Titus Josef Brinker (TJ)

Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.

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Classifications MeSH