Background: Predictive models in surgery promise to improve clinical care by anticipating complications, guiding decision-making, and supporting personalized treatment strategies. Although their potential to enhance outcomes and efficiency is substantial, their integration into clinical practice also raises profound ethical challenges. Ethical Framework: These challenges span the entire lifecycle of predictive models from data collection and development to validation and clinical use. They touch upon patient privacy, algorithmic bias, transparency, and the shifting responsibilities of clinicians. Importantly, the ethical concerns are not isolated to one group but shared across patients, developers, and clinicians within a dynamic stakeholder relationship. Analysis: Key risks include biased or unrepresentative datasets, privacy breaches, opaque decision-making processes, and the danger of deskilling surgeons if reliance on algorithms becomes excessive. To mitigate these risks, strategies, such as out-of-distribution detection, standardized data collection, parallel model development, and continuous auditing, are essential. Beyond technical safeguards, embedding predictive models within a framework of accountability and patient-centered care is necessary to sustain trust and equity. Conclusion: The integration of predictive models into surgery requires more than technical excellence, and it demands ethical vigilance. Preparing future clinicians through education that emphasizes both clinical reasoning and ethical awareness is critical. By aligning predictive model development with human-centered values, healthcare systems can ensure that these innovations enhance surgical practice while safeguarding equity, transparency, and patient trust.

Exoskeletons are technologies that can help to increase or improve mobility, dexterity, and strength. They can be used as assistive devices, to restore lost affordances, or for rehabilitation. While mechanical exoskeletons are passive and rely on the body's power for movement, powered exoskeletons are active mechanical systems that can assist or enhance a user's capacity, including in strength and performance. They also offer scope to augment or enhance beyond simple medical support, with potential in the future for superhuman power and strength. While these technologies present promising clinical opportunities, including for those who want to regain walking capacity, they also bring ethical questions, such as about data privacy and accessibility. In addition, the physical features of the technology can prove mentally, physically, and financially demanding, and may be deployed in contexts where user choice and autonomy is constrained. In this article, we discuss these issues, and raise some pertinent ethical questions, not all of which can be easily answered. We touch upon medical and therapeutic uses, for industrial and workplace settings, and in military contexts specially, given these are contexts where such technology may be required or even imposed. We argue that reasonable optimism for such technologies needs to be tempered by sufficient ethical assessment to identify and address barriers to research, development, and use. As well as managing any impacts and expectations for the health and wellbeing of users, the potential impact on autonomy and the risk of coercion, we have to consider what kind of data may be recorded or used, and the risk that these technologies could exacerbate existing inequalities or harms.

This paper provides a justificatory rationale for recommending the inclusion of imagined future use cases in neurotechnology development processes, specifically for legal and policy ends. Including detailed imaginative engagement with future applications of neurotechnology can serve to connect ethical, legal, and policy issues potentially arising from the translation of brain stimulation research to the public consumer domain. Futurist scholars have for some time recommended approaches that merge creative arts with scientific development in order to theorise possible futures toward which current trends in technology development might be steered. Taking a creative, imaginative approach like this in the neurotechnology context can help move development processes beyond considerations of device functioning, safety, and compliance with existing regulation, and into an active engagement with potential future dynamics brought about by the emergence of the neurotechnology itself. Imagined scenarios can engage with potential consumer uses of devices that might come to challenge legal or policy contexts. An anticipatory, creative approach can imagine what such uses might consist in, and what they might imply. Justifying this approach also prompts a co-responsibility perspective for policymaking in technology contexts. Overall, this furnishes a mode of neurotechnology’s emergence that can avoid crises of confidence in terms of ethico-legal issues, and promote policy responses balanced between knowledge, values, protected innovation potential, and regulatory safeguards.

This article examines the idea of mind-reading technology by focusing on an interesting case of applying a large language model (LLM) to brain data. On the face of it, experimental results appear to show that it is possible to reconstruct mental contents directly from brain data by processing via a chatGPT-like LLM. However, the author argues that this apparent conclusion is not warranted. Through examining how LLMs work, it is shown that they are importantly different from natural language. The former operates on the basis of nonrational data transformations based on a large textual corpus. The latter has a rational dimension, being based on reasons. Using this as a basis, it is argued that brain data does not directly reveal mental content, but can be processed to ground predictions indirectly about mental content. The author concludes that this is impressive but different in principle from technology-mediated mind reading. The applications of LLM-based brain data processing are nevertheless promising for speech rehabilitation or novel communication methods.

This paper asks what sorts of ethical caution ought to attach to increasingly data-driven approaches to understanding the brain. This is taken to be an important question especially owing to a likely near future of neuromonitoring and neuromodulation devices with applications in psychiatry. The paper explores this by i) sketching the concept of ‘digital twin,’ ii) drawing a schematic picture of ‘brain datafication’ in general, and iii) developing a means of understanding some challenges present in datafication through the lens of digital twins. One central concern arises from the role algorithmic processing of neural recordings plays in terms of neuroscientific objectivity, with knock on effects for psychiatric ethics. Essentially, this is owing to a way in which algorithmic processing in brain data construction appears to be deductive in character, but is in fact based on a particular scheme of inductive inference. The challenges explored urge ethical caution as they concern epistemological gaps in data-centered neuroscientific progress, as well as knock-on effects for psychiatry.

This paper examines the use of artificial intelligence (AI) for the diagnosis of autism spectrum disorder (ASD, hereafter autism). In so doing we examine some problems in existing diagnostic processes and criteria, including issues of bias and interpretation, and on concepts like the ‘double empathy problem’. We then consider how novel applications of AI might contribute to these contexts. We're focussed specifically on adult diagnostic procedures as childhood diagnosis is already well covered in the literature.

Implantable brain-computer interfaces (BCIs) are being developed to restore speech capacity for those who are unable to speak. Patients with locked-in syndrome or amyotrophic lateral sclerosis could be able to use covert speech – vividly imagining saying something without actual vocalisation – to trigger neural controlled systems capable of synthesising speech. User control has been identified as particularly pressing for this type of BCI. The incorporation of machine learning and statistical language models into the decoding process introduces a contribution to (or ‘shaping of’) the output that is beyond the user’s control. Whilst this type of ‘shared control’ of BCI action is not unique to speech BCIs, the automated shaping of what a user ‘says’ has a particularly acute ethical dimension, which may differ from parallel concerns surrounding automation in movement BCIs. This paper provides an analysis of the control afforded to the user of a speech BCI of the sort under development, as well as the relationships between accuracy, control, and the user’s ownership of the speech produced. Through comparing speech BCIs with BCIs for movement, we argue that, whilst goal selection is the more significant locus of control for the user of a movement BCI, control over process will be more significant for the user of the speech BCI. The design of the speech BCI may therefore have to trade off some possible efficiency gains afforded by automation in order to preserve sufficient guidance control necessary for users to express themselves in ways they prefer. We consider the implications for the speech BCI user’s responsibility for produced outputs and their ownership of token outputs. We argue that these are distinct assessments. Ownership of synthetic speech concerns whether the content of the output sufficiently represents the user, rather than their morally relevant, causal role in producing that output.