Synthetic Health Data: Real Ethical Promise and Peril.

Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while-potentially-mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it.

Impact of personalized risk scores on shared decision making in left ventricular assist device implantation: Findings from a qualitative study.

Objective: To assess stakeholders' perspectives on integrating personalized risk scores (PRS) into left ventricular assist device (LVAD) implantation decisions and how these perspectives might impact shared decision making (SDM).

Methods: We conducted 40 in-depth interviews with physicians, nurse coordinators, patients, and caregivers about integrating PRS into LVAD implantation decisions. A codebook was developed to identify thematic patterns, and quotations were consolidated for analysis.

Patient Consent and The Right to Notice and Explanation of AI Systems Used in Health Care.

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation.

Deep Brain Stimulation for Childhood Treatment-Resistant Obsessive-Compulsive Disorder: Mental Health Clinician Views on Candidacy Factors.

Introduction: Deep brain stimulation (DBS) is approved under a humanitarian device exemption to manage treatment-resistant obsessive-compulsive disorder (TR-OCD) in adults. It is possible that DBS may be trialed or used clinically off-label in children and adolescents with TR-OCD in the future. DBS is already used to manage treatment-resistant childhood dystonia.

Adolescent OCD Patient and Caregiver Perspectives on Identity, Authenticity, and Normalcy in Potential Deep Brain Stimulation Treatment.

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.

Ethical considerations for integrating multimodal computer perception and neurotechnology.

Background: Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring.

Advances in prosthetic technology: a perspective on ethical considerations for development and clinical translation.

Technological advancements of prostheses in recent years, such as haptic feedback, active power, and machine learning for prosthetic control, have opened new doors for improved functioning, satisfaction, and overall quality of life. However, little attention has been paid to ethical considerations surrounding the development and translation of prosthetic technologies into clinical practice. This article, based on current literature, presents perspectives surrounding ethical considerations from the authors' multidisciplinary views as prosthetists (HG, AM, CLM, MGF), as well as combined research experience working directly with people using prostheses (AM, CLM, MGF), wearable technologies for rehabilitation (MGF, BN), machine learning and artificial intelligence (BN, KKQ), and ethics of advanced technologies (KKQ).

Patients' and physicians' beliefs and attitudes towards integrating personalized risk estimates into patient education about left ventricular assist device therapy.

Background: Personalized risk (PR) estimates may enhance clinical decision making and risk communication by providing individualized estimates of patient outcomes. We explored stakeholder attitudes toward the utility, acceptability, usefulness and best-practices for integrating PR estimates into patient education and decision making about Left Ventricular Assist Device (LVAD).

Methods And Results: As part of a 5-year multi-institutional AHRQ project, we conducted 40 interviews with stakeholders (physicians, nurse coordinators, patients, and caregivers), analyzed using Thematic Content Analysis.

Trust criteria for artificial intelligence in health: normative and epistemic considerations.

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.

Hope and Optimism in Pediatric Deep Brain Stimulation: Key Stakeholder Perspectives.

Introduction: Deep brain stimulation (DBS) is utilized to treat pediatric refractory dystonia and its use in pediatric patients is expected to grow. One important question concerns the impact of hope and unrealistic optimism on decision-making, especially in "last resort" intervention scenarios such as DBS for refractory conditions.

Objective: This study examined stakeholder experiences and perspectives on hope and unrealistic optimism in the context of decision-making about DBS for childhood dystonia and provides insights for clinicians seeking to implement effective communication strategies.

Divergence Between Clinician and Patient Perspectives on Polygenic Embryo Screening: A Qualitative Study.

Objective: To explore and compare the perspectives of clinicians and patients on polygenic embryo screening.

Design: Qualitative.

Subjects: Fifty-three participants: 27 reproductive endocrinology and infertility specialists and 26 patients currently undergoing in vitro fertilization or had done so within the last five years.

Deep Brain Stimulation for Pediatric Dystonia: Clinicians' Perspectives on the Most Pressing Ethical Challenges.

Introduction: Pediatric deep brain stimulation (pDBS) is commonly used to manage treatment-resistant primary dystonias with favorable results and more frequently used for secondary dystonia to improve quality of life. There has been little systematic empirical neuroethics research to identify ethical challenges and potential solutions to ensure responsible use of DBS in pediatric populations.

Methods: Clinicians (n = 29) who care for minors with treatment-resistant dystonia were interviewed for their perspectives on the most pressing ethical issues in pDBS.

Clinician Perspectives on Levels of Evidence and Oversight for Deep Brain Stimulation for Treatment-Resistant Childhood OCD.

Approximately 10-20% of children with obsessive-compulsive disorder (OCD) have treatment-resistant presentations, and there is likely interest in developing interventions for this patient group, which may include deep brain stimulation (DBS). The World Society for Stereotactic and Functional Neurosurgery has argued that at least two successful randomized controlled trials should be available before DBS treatment for a psychiatric disorder is considered "established." The FDA approved DBS for adults with treatment-resistant OCD under a humanitarian device exemption (HDE) in 2009, which requires that a device be used to manage or treat a condition impacting 8,000 or fewer patients annually in the United States.

Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other Large Language Models: Do We Still Need It?

In this paper, we contend with whether we still need traditional ethics education as part of healthcare professional training given the abilities of chatGPT (generative pre-trained transformer) and other large language models (LLM). We reflect on common programmatic goals to assess the current strengths and limitations of LLMs in helping to build ethics competencies among future clinicians. Through an actual case analysis, we highlight areas in which chatGPT and other LLMs are conducive to common bioethics education goals.

Ethical hazards of health data governance in the metaverse.

Metaverse-enabled healthcare is no longer hypothetical. Developers must now contend with ethical, legal and social hazards if they are to overcome the systematic inefficiencies and inequities that exist for patients who seek care in the real world.

Participant perceptions of changes in psychosocial domains following participation in an adaptive deep brain stimulation trial.

Background: There has been substantial controversy in the neuroethics literature regarding the extent to which deep brain stimulation (DBS) impacts dimensions of personality, mood, and behavior.

Objective/hypothesis: Despite extensive debate in the theoretical literature, there remains a paucity of empirical data available to support or refute claims related to the psychosocial changes following DBS.

Methods: A mixed-methods approach was used to examine the perspectives of patients who underwent DBS regarding changes to their personality, authenticity, autonomy, risk-taking, and overall quality of life.

AI in the hands of imperfect users.

As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML's human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.

A Call for Behavioral Science in Embedded Bioethics.

Bioethicists today are taking a greater role in the design and implementation of emerging technologies by "embedding" within the development teams and providing their direct guidance and recommendations. Ideally, these collaborations allow ethical considerations to be addressed in an active, iterative, and ongoing process through regular exchanges between ethicists and members of the technological development team. This article discusses a challenge to this embedded ethics approach-namely, that bioethical guidance, even if embraced by the development team in theory, is not easily actionable in situ.

Post-trial access in implanted neural device research: Device maintenance, abandonment, and cost.

Background: Clinical trial participants who benefit from experimental neural devices for the treatment of debilitating and otherwise treatment-resistant conditions are generally not ensured continued access to effective therapy or maintenance of devices at the conclusion of trials.

Objective/hypothesis: Post-trial obligations have been extensively examined in the context of drug trials, but there has been little empirical examination of stakeholder perspectives regarding these obligations in the rapidly growing field of neural device research.

Methods: This study examined the perspectives of 44 stakeholders (i.

Researchers' Ethical Concerns About Using Adaptive Deep Brain Stimulation for Enhancement.

The capacity of next-generation closed-loop or adaptive deep brain stimulation devices (aDBS) to read (measure neural activity) and write (stimulate brain regions or circuits) shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively (non-therapeutically) modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies (e.g.