Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants’ locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant’s real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by (...) a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas. (shrink)

Research on psychedelic medicines is experiencing a revival. Some clinicians, scientists, and ethicists believe that psychedelics are so different from other treatments that they warrant special consideration in how they are researched, regulated, commercialized, and administered. Others argue that psychedelic medicines show clinical potential, but they should be treated like other medical interventions. In other words, identical standards should apply. This article analyzes whether psychedelic medicines warrant special consideration from a regulatory and ethical perspective.

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order (...) to provide an effective mechanism of oversight for health‐related AI research. (shrink)