Direct-to-consumer medical machine learning and artificial intelligence applications
Direct-to-consumer medical machine learning and artificial intelligence applications
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DOI：
10.1038/s42256-021-00331-0
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发表时间：
2021-04-01
影响因子：
23.8
通讯作者：
Cohen, I. Glenn
中科院分区：
文献类型：
--
作者：
Babic, Boris;Gerke, Sara;Cohen, I. Glenn
直接面向消费者的医疗人工智能/机器学习应用程序越来越多地用于各种诊断评估，而在COVID-19大流行期间对远程医疗和家庭医疗保健的重视可能会进一步刺激其采用。从这个角度来看，我们认为，当一个系统是为临床医生/医生设计的，而不是为个人使用设计的时，人工智能/机器学习监管环境应该以不同的方式运作。直接面向消费者的应用程序引起了独特的关注，由于消费者用户的性质，他们往往是有限的，在他们的统计和医学知识和风险规避他们的健康结果。这就创造了一个假警报可能激增并给公共医疗保健系统和医疗保险公司带来负担的环境。虽然在医学的其他领域也存在类似的情况，但人工智能/机器学习应用程序的易用性和使用频率，以及它们在消费者市场中的日益普及，都需要仔细思考如何有效地监管它们。我们建议监管机构应该努力更好地了解消费者如何与直接面向消费者的医疗人工智能/机器学习应用程序进行交互，特别是诊断应用程序，这需要关注系统的技术规格。我们进一步认为，最好的监管审查也应该考虑这些技术在广泛使用下的社会成本。
Direct-to-consumer medical artificial intelligence/machine learning applications are increasingly used for a variety of diagnostic assessments, and the emphasis on telemedicine and home healthcare during the COVID-19 pandemic may further stimulate their adoption. In this Perspective, we argue that the artificial intelligence/machine learning regulatory landscape should operate differently when a system is designed for clinicians/doctors as opposed to when it is designed for personal use. Direct-to-consumer applications raise unique concerns due to the nature of consumer users, who tend to be limited in their statistical and medical literacy and risk averse about their health outcomes. This creates an environment where false alarms can proliferate and burden public healthcare systems and medical insurers. While similar situations exist elsewhere in medicine, the ease and frequency with which artificial intelligence/machine learning apps can be used, and their increasing prevalence in the consumer market, calls for careful reflection on how to effectively regulate them. We suggest regulators should strive to better understand how consumers interact with direct-to-consumer medical artificial intelligence/machine learning apps, particularly diagnostic ones, and this requires more than a focus on the system's technical specifications. We further argue that the best regulatory review would also consider such technologies' social costs under widespread use.