Labeling of Direct-to-Consumer Medical Artificial Intelligence Applications for “Self-Diagnosis”

Artificial intelligence (AI) is changing our daily life and the way we receive health care. For example, Google hopes to soon start a pilot study for its “AI-powered dermatology tool,” an app with knowledge of 288 skin conditions. The FDA has also already permitted the marketing of similar medical devices, such as Apple’s electrocardiogram (ECG) app. Interestingly, both Google and Apple advertise their direct-to-patient/consumer (DTP/DTC) apps as information tools only that are not intended to provide a diagnosis. This is due to their “over-the-counter” nature, although Apple’s clinical study of the ECG app, for example, correctly diagnosed atrial fibrillation with 98.3 percent sensitivity and 99.6 percent specificity. But do patients and consumers really understand that such and similar medical apps do not replace traditional diagnosis and treatment methods? Moreover, many DTP/DTC medical AI apps for “self-diagnosis” are opaque (“black boxes”), can continuously learn, and are vulnerable to biases. Patients and consumers need to understand the indications for use, the model characteristics, and the risks and limitations of such tools. However, the FDA has not yet developed any labeling standards specifically for AI-based medical devices, let alone for those directly addressed to patients/consumers. This chapter explores not only the benefits of labeling, such as helping patients and consumers to make more informed decisions, but also the potential limitations. It also makes suggestions on the content of labeling for DTP/DTC AI diagnosis apps. In particular, this chapter argues that the advertisement of this technology as “information tools only” rather than “diagnosis tools” is misleading for consumers and patients.