Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.
Department of Dermatology, Heidelberg University, Mannheim, Germany.
JMIR Mhealth Uhealth. 2021 Aug 27;9(8):e22909. doi: 10.2196/22909.
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.
人工智能（AI）已显示出改善各种疾病诊断的潜力，特别是在皮肤癌的早期检测方面。目前尚不清楚 AI 技术在临床实践中的明确应用，也无法确定其对年轻用户群体的附加价值。只有在潜在用户接受的情况下，基于 AI 的诊断工具的翻译才能成功。作为数字原生代的年轻人可能为 AI 成功应用于临床实践提供最大的潜力，同时他们也是未来参与皮肤癌筛查的一代人。
我们进行了一项匿名在线调查，以了解个人接受基于 AI 的移动应用程序进行皮肤癌诊断的意愿和程度。我们评估了偏好和关注的相对影响，重点关注年轻群体。
我们通过 Facebook、LinkedIn 和 Xing 这三个社交媒体渠道招募 35 岁以下的参与者。我们对参与者对皮肤检查移动应用程序的态度进行了描述性分析和统计检验。我们整合了适应性选择式联合分析来评估参与者的偏好。我们使用最大差异标度法评估潜在关注。
我们共纳入 728 名参与者进行分析。大多数参与者（66.5%，484/728；95%置信区间 0.631-0.699）对使用基于 AI 的应用程序持积极态度。特别是居住在大城市或小镇的参与者（P=.02）和熟悉使用健康或健身应用程序的参与者（P=.02），对移动诊断系统的接受程度显著更高。对 484 名持积极态度的参与者的偏好进行分层贝叶斯估计，结果显示，使用移动应用程序作为辅助系统更受欢迎。参与者排除了准确率≤65%的应用程序版本、未加密数据存储的应用程序，以及不提供决策过程背景信息的系统。但是，参与者并不介意其数据匿名用于研究目的，也不反对将临床患者信息纳入决策过程。对 244 名持否定态度的参与者群体进行最大差异标度分析，结果表明，数据安全性、对应用程序的信任度不足以及缺乏个人互动是对应用程序使用的主要关注点。
大多数 35 岁以下的潜在未来用户准备接受基于 AI 的诊断解决方案，以早期发现皮肤癌。然而，为了将其转化为临床实践，参与者对增加基于 AI 的工具透明度和可解释性的需求似乎至关重要。总的来说，18 至 24 岁和 25 至 34 岁的数字原生代在与彼此以及与之前包含其他年龄组的研究结果进行比较时，表现出相似的偏好和关注。
