BackgroundDeep brain stimulation is approved for treating refractory obsessive-compulsive disorder in adults under the US Food and Drug Administration Humanitarian Device Exemption, and studies hav...

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is properly calibrated to a tool’s computational capacities and limitations has both practical and ethical implications, given that overtrust or undertrust can influence over-reliance or under-reliance on algorithmic tools, with significant implications for patient safety and health outcomes. It is, thus, important to better understand how variability in trust (...) criteria across stakeholders, settings, tools and use cases may influence approaches to using AI/ML tools in real settings. As part of a 5-year, multi-institutional Agency for Health Care Research and Quality-funded study, we identify trust criteria for a survival prediction algorithm intended to support clinical decision-making for left ventricular assist device therapy, using semistructured interviews (n=40) with patients and physicians, analysed via thematic analysis. Findings suggest that physicians and patients share similar empirical considerations for trust, which were primarilyepistemicin nature, focused on accuracy and validity of AI/ML estimates. Trust evaluations considered the nature, integrity and relevance of training data rather than the computational nature of algorithms themselves, suggesting a need to distinguish ‘source’ from ‘functional’ explainability. To a lesser extent, trust criteria were also relational (endorsement from others) and sometimes based on personal beliefs and experience. We discuss implications for promoting appropriate and responsible trust calibration for clinical decision-making use AI/ML. (shrink)

The ongoing debate within neuroethics concerning the degree to which neuromodulation such as deep brain stimulation (DBS) changes the personality, identity, and agency (PIA) of patients has paid relatively little attention to the perspectives of prospective patients. Even less attention has been given to pediatric populations. To understand patients’ views about identity changes due to DBS in obsessive-compulsive disorder (OCD), the authors conducted and analyzed semistructured interviews with adolescent patients with OCD and their parents/caregivers. Patients were asked about projected impacts (...) to PIA generally due to DBS. All patient respondents and half of caregivers reported that DBS would impact patient self-identity in significant ways. For example, many patients expressed how DBS could positively impact identity by allowing them to explore their identities free from OCD. Others voiced concerns that DBS-related resolution of OCD might negatively impact patient agency and authenticity. Half of patients expressed that DBS may positively facilitate social access through relieving symptoms, while half indicated that DBS could increase social stigma. These views give insights into how to approach decision-making and informed consent if DBS for OCD becomes available for adolescents. They also offer insights into adolescent experiences of disability identity and “normalcy” in the context of OCD. (shrink)

In their article, Cho and Martinez-Martin (2023) argue that developers and users of digital simulacra for modelling health and disease should involve a continued focus on causality of health states...