PDF(Pubmed)
Abstract：
BACKGROUND: There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system.
OBJECTIVE: The aim of this study was two-fold: (1) to determine the relative importance of the provider (in-person physician, physician via teledermatology, AI, personalized AI), costs of screening (free, 10€, 25€, 40€; 1€=US $1.09), and waiting time (immediate, 1 day, 1 week, 4 weeks) as attributes contributing to patients\' choices of a particular mode of skin cancer screening; and (2) to investigate whether sociodemographic characteristics, especially age, were systematically related to participants\' individual choices.
METHODS: A choice-based conjoint analysis was used to examine the acceptance of medical AI for a skin cancer screening from the patient\'s perspective. Participants responded to 12 choice sets, each containing three screening variants, where each variant was described through the attributes of provider, costs, and waiting time. Furthermore, the impacts of sociodemographic characteristics (age, gender, income, job status, and educational background) on the choices were assessed.
RESULTS: Among the 383 clicks on the survey link, a total of 126 (32.9%) respondents completed the online survey. The conjoint analysis showed that the three attributes had more or less equal importance in contributing to the participants\' choices, with provider being the most important attribute. Inspecting the individual part-worths of conjoint attributes showed that treatment by a physician was the most preferred modality, followed by electronic consultation with a physician and personalized AI; the lowest scores were found for the three AI levels. Concerning the relationship between sociodemographic characteristics and relative importance, only age showed a significant positive association to the importance of the attribute provider (r=0.21, P=.02), in which younger participants put less importance on the provider than older participants. All other correlations were not significant.
CONCLUSIONS: This study adds to the growing body of research using choice-based experiments to investigate the acceptance of AI in health contexts. Future studies are needed to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated with this decision.
OBJECTIVE: The aim of this study was two-fold: (1) to determine the relative importance of the provider (in-person physician, physician via teledermatology, AI, personalized AI), costs of screening (free, 10€, 25€, 40€; 1€=US $1.09), and waiting time (immediate, 1 day, 1 week, 4 weeks) as attributes contributing to patients\' choices of a particular mode of skin cancer screening; and (2) to investigate whether sociodemographic characteristics, especially age, were systematically related to participants\' individual choices.
METHODS: A choice-based conjoint analysis was used to examine the acceptance of medical AI for a skin cancer screening from the patient\'s perspective. Participants responded to 12 choice sets, each containing three screening variants, where each variant was described through the attributes of provider, costs, and waiting time. Furthermore, the impacts of sociodemographic characteristics (age, gender, income, job status, and educational background) on the choices were assessed.
RESULTS: Among the 383 clicks on the survey link, a total of 126 (32.9%) respondents completed the online survey. The conjoint analysis showed that the three attributes had more or less equal importance in contributing to the participants\' choices, with provider being the most important attribute. Inspecting the individual part-worths of conjoint attributes showed that treatment by a physician was the most preferred modality, followed by electronic consultation with a physician and personalized AI; the lowest scores were found for the three AI levels. Concerning the relationship between sociodemographic characteristics and relative importance, only age showed a significant positive association to the importance of the attribute provider (r=0.21, P=.02), in which younger participants put less importance on the provider than older participants. All other correlations were not significant.
CONCLUSIONS: This study adds to the growing body of research using choice-based experiments to investigate the acceptance of AI in health contexts. Future studies are needed to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated with this decision.
摘要：
背景：使用人工智能（AI）筛查皮肤癌引起了极大的兴趣。这是由于皮肤癌发病率上升和训练有素的皮肤科医生日益稀缺。能够识别黑色素瘤的AI系统可以挽救生命，允许立即访问放映，减少不必要的护理和医疗费用。虽然这种基于人工智能的系统从公共卫生的角度来看是有用的，过去的研究表明，个体患者对于接受AI系统检查非常犹豫。
目的：这项研究的目的是双重的：（1）确定提供者的相对重要性（当面医生，医生通过远程皮肤病学，AI,个性化AI)，筛查费用(免费，10€,25€,40欧元；1欧元=1.09美元)，和等待时间(立即，1天,1周,4周)作为有助于患者选择特定皮肤癌筛查模式的属性；和(2)调查社会人口统计学特征是否，尤其是年龄,与参与者的个人选择有系统地相关。
方法：使用基于选择的联合分析从患者的角度检查医学AI对皮肤癌筛查的接受程度。参与者回答了12个选择集，每个包含三个筛选变体，通过提供者的属性描述每个变体，成本,和等待时间。此外，社会人口学特征的影响(年龄，性别,收入,工作状态,和教育背景)对选择进行了评估。
结果：在调查链接的383次点击中，共有126名(32.9%)受访者完成了在线调查。联合分析表明，这三个属性在促进参与者的选择方面或多或少具有同等的重要性，提供程序是最重要的属性。检查联合属性的各个部分价值表明，医生的治疗是最优选的方式，其次是与医生的电子咨询和个性化AI;在三个AI级别中得分最低。关于社会人口统计学特征和相对重要性之间的关系，只有年龄与属性提供者的重要性呈显著正相关(r=0.21，P=.02)，年轻的参与者对提供者的重视程度低于年长的参与者。所有其他相关性均不显著。
结论：这项研究增加了越来越多的研究，使用基于选择的实验来调查AI在健康环境中的接受度。未来的研究需要探索人工智能被接受或拒绝的原因，以及社会人口统计学特征是否与这一决定相关。
目的：这项研究的目的是双重的：（1）确定提供者的相对重要性（当面医生，医生通过远程皮肤病学，AI,个性化AI)，筛查费用(免费，10€,25€,40欧元；1欧元=1.09美元)，和等待时间(立即，1天,1周,4周)作为有助于患者选择特定皮肤癌筛查模式的属性；和(2)调查社会人口统计学特征是否，尤其是年龄,与参与者的个人选择有系统地相关。
方法：使用基于选择的联合分析从患者的角度检查医学AI对皮肤癌筛查的接受程度。参与者回答了12个选择集，每个包含三个筛选变体，通过提供者的属性描述每个变体，成本,和等待时间。此外，社会人口学特征的影响(年龄，性别,收入,工作状态,和教育背景)对选择进行了评估。
结果：在调查链接的383次点击中，共有126名(32.9%)受访者完成了在线调查。联合分析表明，这三个属性在促进参与者的选择方面或多或少具有同等的重要性，提供程序是最重要的属性。检查联合属性的各个部分价值表明，医生的治疗是最优选的方式，其次是与医生的电子咨询和个性化AI;在三个AI级别中得分最低。关于社会人口统计学特征和相对重要性之间的关系，只有年龄与属性提供者的重要性呈显著正相关(r=0.21，P=.02)，年轻的参与者对提供者的重视程度低于年长的参与者。所有其他相关性均不显著。
结论：这项研究增加了越来越多的研究，使用基于选择的实验来调查AI在健康环境中的接受度。未来的研究需要探索人工智能被接受或拒绝的原因，以及社会人口统计学特征是否与这一决定相关。
