BACKGROUND
Artificial Intelligence (AI) has shown potential to improve diagnostics of various diseases and especially early skin cancer detection. What is missing is the bridge from AI technology to clear application scenarios in clinical practice as well as added value for patients. 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 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 applications for skin cancer diagnostics. In this way, we evaluated the preferences as well as the relative influence of concerns with a special focus on younger age groups.
METHODS
We recruited respondents below 35 years of age through the social media channels Facebook, LinkedIn and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes towards mobile applications for skin examination. An adaptive choice-based conjoint (ACBC) was integrated to assess respondents’ preferences. Potential concerns were evaluated using maximum difference scaling (MaxDiff).
RESULTS
728 respondents were included in the analysis. About 66.5% expressed a positive attitude towards the use of AI-based applications. In particular participants residing in big cities or small towns and individuals that were familiar with the use of health or tracking apps were significantly more open towards mobile diagnostic systems. Hierarchical Bayes estimation (HB) of the preferences of participants with positive attitude (n=484) revealed that the use of mobile applications as an assistance system was preferred. Respondents ruled out app versions with an accuracy of 65 percent or less, applications using data storage without encryption as well as systems that did not deliver background information about the decision-making. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information into the decision-making process. MaxDiff analysis for the negative-minded participant group (n=244) outlined that data security, insufficient trust in the app, as well as the 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 was ready to accept AI-based diagnostic solutions for early skin cancer detection. However, for translation into clinical practice, participants’ demand for increased transparency and explainability of AI-based tools seems to be critical. Altogether, digital natives expressed similar preferences and concerns when compared to results obtained by previous studies that included other age groups.
Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)
