Since its introduction, electroconvulsive therapy (ECT) has been controversial. Recently, a report by the World Health Organization called for an ECT ban for children and adolescents. This has added a new layer to consider in the debate about the ethical use of ECT. To better understand ethical concerns and attitudes toward the use of ECT in this population, and to assess whether a ban might be justified, we conducted a comprehensive literature review. Close to 75% of our analyzed papers reported agreement about ECT's effectiveness for treating a wide range of adolescent mental disorders. Almost half of the papers reported memory side effects as an ethical concern. In addition, the lack of rigorous side-effect reporting was found to be a key barrier to the ethical use of ECT in this population. We hope this review will advance our understanding of ongoing ethical concerns and barriers regarding use of ECT in adolescents.

Mental health disorders are a significant public health problem with profound impacts on individual health, communities and society. Interventional approaches are limited in both utilization and access, despite their strong evidence base in specific disorders. The objective of this review is to describe factors and challenges influencing equity in interventional psychiatry. Mental health care inequities are pervasive and persistent. For interventional psychiatry tools, even with current evidence of interventions’ cost-effectiveness, inequities in access persist based on geographic context, socioeconomic status, and race, as well as insurers’ reluctance to cover some of them. A wide range of social factors impact access and utilization of interventional psychiatry tools. To ensure parity, we need strategies to increase awareness and to address access and utilization disparities found across these interventions, in addition to keeping up-to-date mental health coverage policies based on new safety and effectiveness evidence.

Multimodal artificial intelligence (MMAI) is transforming biomedicine by integrating heterogeneous data, e.g., images, speech, behavior, physiological signals, and text, into unified representational spaces. This enables powerful cross-modal inference and data synthesis, with potential gains in diagnostic accuracy, early detection, and patient support. However, these capabilities introduce ethical challenges that exceed existing AI governance frameworks. MMAI can infer sensitive information without patient awareness, and can convert such inferences into new data objects (e.g., images, clinical text) that enter medical records without clear provenance, acquiring the practical status of observed clinical facts. This raises ethical concerns around the infrastructural emedding of inference-based data objects as durable, reusable clinical and research data. The procedures and technical pipelines that govern how such data are classified and integrated into clinical and research infrastructures embed consequential decisions about provenance, attribution, and contestability, often made in advance of adequate governance. In this Perspective, we characterize what distinguishes MMAI-generated data from other forms of algorithmic inference and argue that MMAI is ethically novel in part because it renders cross-modal inferences as recordable data objects, thereby blurring the boundary between observation and generation. We therefore argue for a shift from data-centric protection toward governance of inference and infrastructuring. We propose a four-part agenda: (1) provenance labeling as a prerequisite for accountability; (2) evidence-building to track emergent inference capacities; (3) dynamic consent models responsive to evolving capabilities; and (4) privacy-preserving techniques to limit unjustified or unconsented inferences. These steps aim to support innovation while safeguarding individual rights and expectations.

IntroductionFor people with Parkinson’s disease (PD), deciding whether to pursue deep brain stimulation (DBS) has become increasingly complex. Evidence suggests current approaches to collaborative decision-making may fall short of accepted standards. Thus, a decision support intervention, such as a patient decision aid (PtDA) may be warranted. PtDAs have been shown to improve patients’ knowledge, expectations, and participation in decision-making for other, similar healthcare decisions. We therefore sought to assess neurologists’ awareness of, and experience using, PtDAs, and to solicit their opinions on the ideal features of a PtDA for PD patients considering DBS.MethodsSixteen United States-based neurology clinicians were interviewed about their experiences in treating and counseling PD patients considering DBS. These semi-structured interviews were analyzed using qualitative content analysis.ResultsSeven general neurologists, eight movement disorders specialists, and one registered nurse in a movement practice participated in interviews. None of the clinicians had experience using a patient decision aid, and many were unfamiliar with the concept altogether. All largely expressed optimism about the potential utility of a PtDA for PD patients considering DBS. There was less consensus about the ideal format, content, and implementation of such a PtDA.ConclusionHealthcare providers recognize the potential benefits of a PtDA to engage PD patients considering DBS. However, input from other stakeholders—particularly the patients themselves—is essential to validate these findings.

Deep brain Stimulation (DBS) surgery effectively alleviates troublesome motor symptoms of Parkinson’s disease (PD) such as tremor, rigidity, bradykinesia, motor fluctuations and dyskinesia. It also improves some non-motor symptoms and quality of life. DBS should thus be considered when these symptoms interfere with quality of life despite optimal medical treatment. While DBS benefits are clear, access by eligible patients remains low. Patients and their caregivers should be educated regarding DBS surgery and referrals for surgical evaluation should occur alongside ongoing medication adjustments—particularly when those changes fail to adequately control motor symptoms—regardless of the stage of disease progression. This international panel of DBS experts developed consensus recommendations with the goals of promoting timely referrals and approvals, while reducing misconceptions and stigma associated with brain surgery. These recommendations provide a framework for referring providers, ensuring that appropriate candidates receive timely access to this beneficial treatment.

Implantable brain-computer interfaces (iBCIs) are approaching clinical deployment and can transform care for people with speech and motor impairment by restoring their ability in communication, movement, and aspects of agency. As a means of neurological intervention, iBCIs are not ethically or clinically analogous to current clinically established procedures or devices. They combine invasive neurosurgery, continuous neural data capture, adaptive machine-learning-based decoding, software dependence, and functionality that can change over time. These features create distinctive ethical, legal, clinical, and practical challenges that conventional informed consent frameworks do not adequately address. In this review and analysis, we present a framework and checklist to help guide more consistent and comprehensive informed consent for iBCIs to strengthen respect for autonomy, align stakeholder expectations, reduce fragmentation across sites, and support ethically robust translation of iBCIs into clinical practice.

Background Transcranial Magnetic Stimulation (TMS) is an established treatment for Treatment-Resistant Depression (TRD), yet access and adherence remain challenging. Objective To identify perceived and experienced barriers to TMS and strategies that support treatment completion in a real-world clinical setting. Methods Adults with TRD (N = 38) initiating a standard 6-week TMS course at a Minnesota clinic completed pre- and post-treatment surveys. Measures included TMS knowledge, anticipated and experienced barriers, and open-ended responses about treatment experiences. Quantitative analyses compared barrier rankings across timepoints; qualitative data were thematically analyzed. Results No barrier rankings changed significantly from pre- to post-treatment, though accessibility approached significance. The most common barriers were transportation, time and scheduling demands, cost/insurance concerns, and uncertainty about efficacy. Open-ended responses emphasized the disruptive impact of treatment frequency, TRD symptoms themselves as barriers, and the strain of logistical and financial planning. Supports that facilitated completion included family and workplace flexibility, proactive scheduling, and clinic staff responsiveness. Conclusions Barriers to TMS extend beyond cost to include practical, psychological, and logistical challenges. Individualized support, flexible scheduling, and insurance navigation may reduce burden and improve completion rates. Patient interest in shorter or accelerated TMS (aTMS) highlights the importance of documenting whether emerging protocols reduce or shift these barriers.

Neuroscience's accelerating advances have reached a pivotal point in the study of the human brain, including neurotechnologies capable of recording large amounts of data and acting with greater precision. However, the use of neurotechnology has raised a number of ethical, legal, and social implications (ELSI). To that end, sufficiently robust policy and governance structures must be considered. To date, no published review of United States policies governing neuroscience and neurotechnology exists. To address this, we review US polices and various ethical frameworks overseeing neuroscience and neurotechnology. This policy review highlights where gaps in neuroscience and neurotechnology policy and governance might exist. Overall, our review shows that "soft policies" make up the present-day US neurotech-governance universe at the federal level, with neurodata specific state-legislation emerging. The included analysis can aid researchers, technology developers, neuroethicists, research ethicists, legal scholars, and others in facilitating ethically and socially responsible implementation of neuroscience and neurotechnology as they move from "bench to bedside and beyond."

Synthetic data are increasingly being used in data-driven fields. While synthetic data is a promising tool in medicine, it raises new ethical, legal, and social implications (ELSI) challenges. There is a recognized need for well-designed approaches and standards for documenting and communicating relevant information about artificial intelligence (AI) research datasets and models, including consideration of the many ELSI challenges. This study investigates the ethical dimensions of synthetic data and explores the utility and challenges of ELSI-focused computational checklists for biomedical AI via semi-structure interviews with subject matter experts. Our results suggest that AI experts have tempered views about the promises and challenges of both synthetic data and ELSI-focused computational checklists. Experts discussed a number of ELSI issues covered by previous literature on the topic, such as issues of bias and privacy, yet other less discussed ELSI issues, such as social justice implications and issues of trust were also raised. When discussing ELSI-focused computational checklists our participants highlighted the challenges connected to developing and implementing them.

A significant goal of neuroethics is to offer neuroscientists, health care providers, law- and policy-makers and others, ways of thinking and acting on matters relevant to brain health and conditions that affect the central nervous system. This goal and related calls to action have been derived from theory or empirical work and bring different levels of normative force. To bring the latter in particular to the foreground of discussion, we explored for this Policy Forum different calls to action as they are associated with chosen terminology, the definitions of terms, origins to which they are benchmarked, locations in text, and targeted audiences. We find variability on all of these factors as they appear in the original foundational journals for neuroethics: AJOB Neuroscience and Neuroethics. We recommend that for a field whose very existence relies on uptake of advice, better consistency of language will improve credibility, acceptance, and implementation.

Artificial intelligence (AI) and machine learning (ML) tools are now proliferating in biomedical contexts, and there is no sign this will slow down any time soon. AI/ML and related technologies promise to improve scientific understanding of health and disease and have the potential to spur the development of innovative and effective diagnostics, treatments, cures, and medical technologies. Concerns about AI/ML are prominent, but attention to two specific aspects of AI/ML have so far received little research attention: synthetic data and computational checklists that might promote not only the reproducibility of AI/ML tools but also increased attention to ethical, legal, and social implications (ELSI) of AI/ML tools. We administered a targeted survey to explore these two items among biomedical professionals in the United States. Our survey findings suggest that there is a gap in familiarity with both synthetic data and computational checklists among AI/ML users and developers and those in ethics-related positions who might be tasked with ensuring the proper use or oversight of AI/ML tools. The findings from this survey study underscore the need for additional ELSI research on synthetic data and computational checklists to inform escalating efforts, including the establishment of laws and policies, to ensure safe, effective, and ethical use of AI in health settings.

In recent years, legislators in many states have proposed laws governing the use of psychiatric electroceutical interventions (PEIs), which use electrical or magnetic stimulation to treat mental disorders. To examine how the PEI views of relevant stakeholder groups (e.g., psychiatrists, patients, caregivers, and general public) relate to preferences for proposed policies governing PEI use, we analyze data from a survey on using one of four PEIs to treat major depressive disorder administered to national samples of the stakeholder groups above. We find that the three non-clinician groups’ similar PEI policy preferences differ significantly from those of psychiatrists—with the greatest divide on policies governing the use of electroconvulsive therapy. This divide between psychiatrists’ and non-clinicians’ PEI policy preferences was greater with access-reducing than with access-expanding policies. We advise policymakers to consider such variation in the preferred availability of PEIs across modalities and stakeholder groups when crafting legislation on these interventions.

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. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use.

Psychiatric electroceutical interventions (PEIs) use electrical or magnetic stimulation to treat psychiatric conditions. For depression therapy, PEIs include both approved treatment modalities, such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS), and experimental neurotechnologies, such as deep brain stimulation (DBS) and adaptive brain implants (ABIs). We present results from a survey-based experiment in which members of four relevant stakeholder groups (psychiatrists, patients with depression, caregivers of adults with depression, and the general public) assessed whether treatment with one of four PEIs (ECT, rTMS, DBS, or ABIs) was better or worse than living with treatment-resistant depression (TRD) and then provided a narrative explanation for their assessment. Overall, the prevalence of many narrative themes differed substantially by stakeholder group—with psychiatrists typically offering different reasons for their assessment than non-clinicians—but much less so by PEI modality. A large majority of all participants viewed their assigned PEI as better than living with TRD, with their reasons being a mix of positive views about the treatment and negative views about TRD. The minority of all participants who viewed their assigned PEI as worse than living with TRD tended to express negative affect toward it as well as emphasize its riskiness, negative side effects, and, to a lesser extent, its invasiveness. The richness of these narrative explanations enabled us to put in context and add depth to key patterns seen in recent survey-based research on PEIs.

The aim of this study is to examine ways in which prior experiences and familiarity with psychiatric electroceutical interventions (PEI) shape psychiatrists' and patients' views about these interventions. We administered a national survey, with an embedded experiment, to psychiatrists (n = 505) and adults diagnosed with depression (n = 1050). We randomly assigned respondents to one of 8 conditions using a full factorial experimental design: 4 PEI modalities [ECT, rTMS, DBS, or adaptive brain implants (ABIs)] by 2 depression severity levels [moderate or severe]. We analyzed the survey data with ANOVA and OLS linear regression models. Patients having experience with any PEI reported more positive affect toward, but also greater perceived risk from, their assigned PEI than did patients with no such experience. Psychiatrists who referred or administered any PEI reported more positive affect toward and greater perceived influence on self and perceived benefit from their assigned PEI than did psychiatrists with no such familiarity. Limitations of our study include that our participants were randomly assigned to a PEI, not necessarily to the one they had experience with. Moreover, our study did not directly ask about the kind of experiences participants had with a given PEI. Overall, our survey data shows that greater experience with PEIs elicits more positive affect in both stakeholder groups. Beyond this, prior PEI experience shapes attitudes towards these interventions in complex ways. Further research linking different types of experience with a given PEI would help better understand factors shaping attitudes about specific PEIs.

Advances in medical neurotechnology (MNT) have the potential to improve the evaluation and management of conditions of the nervous system. Meanwhile, increasing concern over ethical questions and risks posed by these technologies has prompted various institutional initiatives to examine ethical aspects of their design, development, and deployment. Funding by public agencies, foundations, and private investors plays a fundamental role in driving MNT research and development (R&D). As such, the perspectives and approaches of funders can be central in instilling and shaping the values embedded in MNTs. Responsible development and oversight of MNT requires funders to proactively prioritize the integration of ethical values in MNT. We propose a multiphase design and development process involving discrete institutional norms and organizational practices and explore how this process can enhance the funder role to address ethical challenges and promote values integration in R&D.

OBJECTIVES:Neurostimulation interventions often face heightened barriers limiting patient access. The objective of this study is to examine different stakeholders' perceived barriers to using different neurostimulation interventions for depression. METHODS:We administered national surveys with an embedded experiment to 4 nationwide samples of psychiatrists (n = 505), people diagnosed with depression (n = 1050), caregivers of people with depression (n = 1026), and members of the general public (n = 1022). We randomly assigned respondents to 1 of 8 conditions using a full factorial experimental design: 4 neurostimulation modalities (electroconvulsive therapy [ECT], repetitive transcranial magnetic stimulation [rTMS], deep brain stimulation [DBS], or adaptive brain implants [ABIs]) by 2 depression severity levels (moderate or severe). We asked participants to rank from a list what they perceived as the top 3 barriers to using their assigned intervention. We analyzed the data with analysis of variance and logistic regression. RESULTS:Nonclinicians most frequently reported "limited evidence of the treatment's effectiveness" and "lack of understanding of intervention" as their top 2 most important practical barriers to using ECT and TMS, respectively. Compared with nonclinicians, psychiatrists were more likely to identify "stigma about treatment" for ECT and "lack of insurance coverage" for TMS as the most important barriers. CONCLUSIONS:Overall, psychiatrists' perceptions of the most important barriers to using neurostimulation interventions were significantly different than those of nonclinicians. Perceived barriers were significantly different for implantable DBS and ABI) versus nonimplantable (rTMS and ECT) neurostimulation interventions. Better understanding of how these barriers vary by neurostimulation and stakeholder group could help us address structural and attitudinal barriers to effective use of these interventions.

IntroductionFirst responders play a pivotal role in ensuring the wellbeing of individuals during critical situations. The demanding nature of their work exposes them to prolonged shifts and unpredictable situations, leading to elevated fatigue levels. Modern countermeasures to fatigue do not provide the best results. This study evaluates the acceptance and ethical considerations of a novel fatigue countermeasure using transcranial Direct Current Stimulation (tDCS) for fire and emergency medical services (EMS) personnel.MethodsTo better understand first responders' perceptions and ethical concerns about this novel fatigue countermeasure in their work, we conducted semi-structured interviews with first responders (N = 20). Interviews were transcribed into text and analyzed using qualitative content analysis.ResultsOver half of responders (59%) were interested, but over a third had a cautionary stand. Half of the participants seemed to have positive views regarding acceptability; a few were more cautionary or hesitant. A main area of consideration was user control (75%), with the majority wanting to retain some control over when or whether to accept the stimulation. Just above half of the participants (64%) mentioned privacy concerns. Another relevant consideration, raised by 50% of participants, was safety and the potential impact of stimulation (e.g., side effects, long-term effects). Overall, participants thought they needed to understand the system better and agreed that more education and training would be required to make people more willing to use it.DiscussionOur exploration into combating fatigue among first responders through tDCS has revealed promising initial reactions from the responder community. Findings from this study lay the groundwork for a promising solution, while still in a nascent design stage, to improve the effectiveness and resilience of first responders in fatiguing shifts and critical situations.

Questions and concerns about artificial intelligence (AI) technologies in education reached a fever pitch with the arrival of publicly accessible, user-facing generative AI systems, especially ChatGPT. Many of these issues will require regulation and collective action to address. But when it comes to generative AI and literacy, we argue that posthuman perspectives can help literacy scholars and practitioners reframe some concerns into questions that open new areas of inquiry. Agential realism in particular offers a useful perspective for exploring how generative AI matters in literacy practices, not as a unilaterally destructive force, but as a set of phenomena that intra-actively reconfigures literacy practices. As a sociocultural (and as we argue, sociotechnical) practice, literacy arises out of the entanglement of bodies, spaces, contexts, positions, histories, and technologies. Generative AI is another in a long line of technologies that reconfigures literacy practices. In this article, we briefly explain how generative AI systems work, focusing on text-based systems called Large Language Models (LLMs), and suggest ways that generative AI may reconfigure the sociocultural practice of literacy. We then offer three provocations to shift discussions about generative AI and literacy (1) from concerns about intentionality to questions of responsibility, (2) from concerns about authenticity to questions of mattering, and (3) from concerns about imitation to questions of multifarious communication. We conclude by encouraging literacy scholars and practitioners to draw inspiration from critical literacy efforts to discover what matters when it comes to generative AI and literacy. When it comes to generative AI and learning, we argue that posthuman perspectives can help literacy scholars and practitioners reframe some concerns into questions that open new areas of inquiry. Drawing on agential realism, we offer three provocations to shift discussions about generative AI and literacy (1) from concerns about intentionality to questions of responsibility, (2) from concerns about authenticity to questions of mattering, and (3) from concerns about imitation to questions of multifarious communication. We conclude by encouraging literacy scholars and practitioners to draw inspiration from critical literacy efforts to discover what matters when it comes to generative AI and literacy.

The demand for meat and seafood products has been globally increasing for decades. To address the environmental, social, and economic impacts of this trend, there has been a surge in the development of three-dimensional (3D) food bioprinting technologies for lab-grown muscle food products and their analogues. This innovative approach is a sustainable solution to mitigate the environmental risks associated with climate change caused by the negative impacts of indiscriminative livestock production and industrial aquaculture. This review article explores the adoption of 3D bioprinting modalities to manufacture lab-grown muscle food products and their associated technologies, cells, and bioink formulations. Additionally, various processing techniques, governing the characteristics of bioprinted food products, nutritional compositions, and safety aspects as well as its relevant ethical and social considerations, were discussed. Although promising, further research and development is needed to meet standards and translate into several industrial areas, such as the food and renewable energy industries. In specific, optimization of animal cell culture conditions, development of serum-free media, and bioreactor design are essential to eliminate the risk factors but achieve the unique nutritional requirements and consumer acceptance. In short, the advancement of 3D bioprinting technologies holds great potential for transforming the food industry, but achieving widespread adoption will require continued innovation, rigorous research, and adherence to ethical standards to ensure safety, nutritional quality, and consumer acceptance.