Alex John London

Background: Because confirmatory clinical trials are costly, large-scale endeavors, the choice of their design carries significant weight. While the current methodological landscape offers tools to address residual pre-trial uncertainty through prespecified adaptations to design elements, design choices remain frequently constrained by prevailing orthodoxies. Methods: We examine how unacknowledged design uncertainties (e.g., sample size calculations based on incorrect effect size or variance estimates) impact the statistical, ethical, and resource-stewardship goals of confirmatory trials. We contrast how fixed and adaptive design strategies handle these uncertainties, detailing the operational safeguards, firewalls, and trade-offs (e.g., complexity, operational bias) required to preserve trial integrity. Results: Strict adherence to default templates prevents stakeholders from matching the design strategy to the specific complexities of the research question. We observe that the gold standard status of fixed designs often obscures their limitations in handling design uncertainties. A rigid adherence to these conventions can ethically hinder a trial’s ability to deliver conclusive results. Conversely, while adaptive designs can offer tools to efficiently reduce uncertainty, we acknowledge that adaptive elements are not without cost; their implementation requires rigorous safeguards to manage specific risks to trial integrity and potential operational biases. Conclusion: To better align clinical research with its ethical and scientific mandates, we issue two calls to action. First, trial designers, including statisticians, must clearly articulate how target and design uncertainties impact ethical obligations, promoting an open-minded evaluation of both fixed and adaptive methods. Second, stakeholders must foreground ethical considerations during design selection, requiring explicit justification for the use—or non-use—of adaptive elements. The ethical path forward is not to default to the old or blindly adopt the new, but to explicitly justify the chosen design based on its responsiveness to the specific uncertainties of the trial. We posit that trial design must transition from a habit-based process to one where pre-trial uncertainties are openly discussed and addressed.

Technologies are being rapidly developed that enable artificial intelligence (AI) systems to dynamically emulate the voice, appearance, and mannerisms of real people. Although concerns about the potential harms from such systems are widespread, there is comparatively little discussion of the conditions under which they might be used to benefit impacted parties. Recent efforts to provide a formal model of benefit represent an advance, but do not represent with sufficient clarity how such systems mediate social relationships in a way that can make a wide range of parties vulnerable to failure modes such as deception and domination. We build on and extend the Beneficent Intelligence (BI) framework from [23] to clarify the mediating role that such AI systems play, how this impacts alignment with the goals of a range of parties, and identify conditions necessary for such systems to confer meaningful benefit. Our analysis can assist developers, users, and other interested parties in better managing the risks associated with emulating technologies.

Microstimulation delivers electrical pulses directly into the brain, with one of its promises being to restore lost senses to millions of people. Yet a fundamental challenge remains: how do intracortical microstimulation (ICMS) patterns engage neural circuits to achieve the inception of specific experiences, such as vivid sensory percepts of touch and vision? Here, we define “inception” as the initiation of percepts evoked by microstimulation through the mapping of stimulation to circuit-level activity that results in sensory experiences. This perspective proposes an integrated research framework that combines Reverse Translation, Forward Translation, and computational neuroscience to bridge insights between clinical observations and high-resolution animal studies. Our framework envisions the development and evaluation of ICMS strategies within a cross-species system that narrows the range of plausible underlying neural mechanisms and the set of evoked perceptual outcomes. Reverse Translation uses human perceptual reports about phosphenes, tones, and touch to guide investigations in rodents and non-human primates (NHPs), mapping the cell types and circuits underlying each percept. Forward Translation leverages these biological insights to design refined ICMS protocols for selective circuit engagement. Bidirectional Translation weaves these approaches together through computational neuroscience, ensuring that experimental observations iteratively and continuously refine one another across species and experimental modalities. This integrated strategy aims to transform microstimulation research into a dynamic dialogue between fundamental science and human experience. Harnessing the Bidirectional Translation Framework can accelerate therapies that enhance quality of life for people with sensory or motor impairments, and contribute more broadly to systems neuroscience by uncovering the mechanisms by which causal manipulation changes activity in neurons and networks.

A ‘silent trial’ refers to the prospective, noninterventional testing of artificial intelligence (AI) models in the intended clinical setting without affecting patient care or institutional operations. The silent evaluation phase has received less attention than in silico algorithm development or formal clinical evaluations, despite its increasing recognition as a critical phase. There are no formal guidelines for performing silent AI evaluations in healthcare settings. We conducted a scoping review to identify silent AI evaluations described in the literature and to summarize current practices for performing silent testing. We screened the PubMed, Web of Science and Scopus databases for articles fitting our criteria for silent AI evaluations, or silent trials, published from 2015 to 2025. A total of 891 articles were identified, of which 75 met the criteria for inclusion in the final review. We found wide variance in terminology, description and rationale for silent evaluations, leading to substantial heterogeneity in the reported information. Overwhelmingly, the papers reported measurements of area under the curve and similar metrics of technical performance. Far fewer studies reported verification of outputs against an in situ clinical ground truth; when reported, the approaches varied in comprehensiveness. We noted less discussion of sociotechnical components, such as stakeholder engagement and human–computer interaction elements. We conclude that there is an opportunity to bring together diverse evaluative practices (for example, from data science, human factors and other fields) if the silent evaluation phase is to be maximally effective. These gaps mirror challenges in the effective translation of AI tools from computer to bedside and identify opportunities to improve silent evaluation protocols that address key needs.

We define an agency transfer point as the point at which a person is no longer capable of performing an important task or function such that responsibility for that task or function must be transferred to another person. Because many older adults worry that if they experience cognitive decline they might lose their ability to function in ways they value, there’s growing interest in the possibility that AI systems might help older adults maintain their independence. This paper identifies several strategies through which AI systems might help older adults avert agency transfer points caused by cognitive decline. Although safe and effective assistive AI systems for older adults do not yet exist, it is important to proactively identify and address problems likely to result from their development and deployment. Importantly, although AI support might avert agency transfer points, there has been little recognition of the prospect that if individuals experience further declines in cognitive capacity, agency transfer points will reemerge. We therefore examine the ethical implications of alternative governance strategies and social policies for coping with the reemergence of agency transfer points for older adults who rely on AI systems to remain independent. This includes consideration of a requirement for contingency planning on the part of older adults as a condition of using such systems. We also consider the role that conversational AI systems might play in the process of contingency planning and after responsibility for some aspects of an older adult’s care has been transferred to another person.

The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum’s capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders’ ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for morally permissible interactions between AI systems and those impacted by their functioning, and two sufficient conditions for realizing the ideal of meaningful benefit. We then contrast this ideal with several salient failure modes, namely, forms of social interactions that constitute unjustified paternalism, coercion, deception, exploitation and domination. The proliferation of incidents involving AI in high-stakes domains underscores the gravity of these issues and the imperative to take an ethics-led approach to AI systems from their inception.