Artificial intelligence pipelines -- spanning data collection, model training, deployment, and post-deployment monitoring -- concentrate ethical risks that intensify with multimodal and agentic systems. Existing governance instruments, including the EU AI Act, the IEEE 7000 series, and the NIST AI Risk Management Framework, provide high-level guidance but often lack enforceable, end-to-end operational controls. This paper presents an ethics-by-design control architecture that embeds consequentialist, deontological, and virtue-ethical reasoning into stage-specific enforcement mechanisms across the AI lifecycle. The framework implements a triple-gate structure at each lifecycle stage: Metric gates (quantitative performance and safety thresholds), Governance gates (legal, rights, and procedural compliance), and Eco gates (carbon and water budgets and sustainability constraints). It specifies measurable trigger conditions, escalation paths, audit artefacts, and mappings to EU AI Act obligations and NIST RMF functions, enabling integration with existing MLOps and CI/CD pipelines. Illustrative examples from large language model pipelines demonstrate how gate-based controls can surface and constrain technical, social, and environmental risks prior to release and during runtime. The framework is accompanied by a preregistered evaluation protocol that defines ex ante success criteria and assessment procedures, enabling falsifiable evaluation of gate effectiveness. By translating normative commitments into enforceable and testable controls, the framework provides a practical basis for operational AI governance across organizational contexts, jurisdictions, and deployment scales.
Ethical debates in AI have primarily focused on back-end issues such as data governance, model training, and algorithmic decision-making. Less attention has been paid to the ethical significance of front-end design choices, such as the interaction and representation-based elements through which users interact with AI systems. This gap is particularly significant for Conversational User Interfaces (CUI) based on Natural Language Processing (NLP) systems, where humanizing design elements such as dialogue-based interaction, emotive language, personality modes, and anthropomorphic metaphors are increasingly prevalent. This work argues that humanization in AI front-end design is a value-driven choice that profoundly shapes users' mental models, trust calibration, and behavioral responses. Drawing on research in human-computer interaction (HCI), conversational AI, and value-sensitive design, we examine how interfaces can play a central role in misaligning user expectations, fostering misplaced trust, and subtly undermining user autonomy, especially in vulnerable contexts. To ground this analysis, we discuss two AI systems developed by Chayn, a nonprofit organization supporting survivors of gender-based violence. Chayn is extremely cautious when building AI that interacts with or impacts survivors by operationalizing their trauma-informed design principles. This Chayn case study illustrates how ethical considerations can motivate principled restraint in interface design, challenging engagement-based norms in contemporary AI products. We argue that ethical front-end AI design is a form of procedural ethics, enacted through interaction choices rather than embedded solely in system logic.
We are currently witnessing a golden era of artificial intelligence for science, marked by a continuous emergence of new algorithms capable of processing diverse biological data sources, and novel tools for uncovering and understanding physiological and pathological knowledge. These advancements are propelling biomedicine towards greater network integration, personalization, and preventive orientation. The paradigm of "data-driven research empowered by artificial intelligence" has become a key strategy for accelerating biomedical discovery. It is reshaping the epistemological frameworks and knowledge production structures of the field by promoting interdisciplinary research models and platform-based organizational forms, thereby fostering global scientific collaboration and transforming the skillsets of biomedical researchers. However, data not only expands the boundaries of cognition, but also creates new epistemic barriers. Now more than ever, biomedicine must respond with caution to the technological and ethical challenges posed by issues, such as medical safety and human–machine value alignment, aiming to avoid adverse clinical outcomes and social impacts.
Abstract The EU Clinical Trials Regulation (CTR) was introduced to harmonize clinical trial evaluations across Member States while upholding participant protection and ethical integrity. This study analyzes 6740 Requests for Information (RFIs) issued by Belgian Medical Research Ethics Committees (MRECs) across 266 trial dossiers evaluated between 2017 and 2024, spanning both the CTR pilot phase and the initial CTIS implementation. Using framework content analysis, we examined the number and content of RFIs in relation to trial outcomes, sponsor type (commercial vs. non-commercial), and the MREC’s role as Reporting Member State (RMS) or Member State Concerned (MSC). Results show a decline in total RFIs over time, mainly due to a reduction in typographical and linguistic remarks, yet significant variability persists in the formulation and scope of ethical feedback. While statistical and methodological concerns remained central in Part I evaluations, RFIs increasingly addressed newer challenges such as decentralized trials, e-consent, and data collection on ethnicity. Part II RFIs continued to focus heavily on informed consent documents. We further observed that MSCs raised fewer RFIs than RMSs for Part I, prompting reflection on the necessity and efficiency of full multi-state review in this section. The study also highlights a growing emphasis on regulatory compliance—sometimes at the expense of ethical deliberation—and the limited authority of policy advisors to correct inconsistencies, despite their expertise. We recommend clearer guidance, formalized roles for policy advisors in quality control, improved pre-submission processes, and limited direct communication between MRECs and sponsors. These findings support ongoing efforts to improve ethics review efficiency and quality under the CTR, with broader relevance for harmonization across Europe.
Christine Bernardi, Daniel Wolff, Florian Lüke
et al.
Abstract Objective According to legal and ethical obligations, patients must be thoroughly informed about the trial in which they could enrol, requiring them to consider and digest a significant amount of complex information. Many cancer patients feel overwhelmed which hinders their ability to make informed decision regarding their care. There is a need for further evidence-based strategies on how to improve both physician-patient-communication and informed consent (IC) documents in this area. We explored the views of experts from various disciplines on communication in IC processes in oncology. Methods Seventeen semi-structured interviews with multidisciplinary experts were conducted and analysed using framework analysis. Results Several experts stated that IC documents have become highly legalistic, often prioritizing the interests of sponsors and further institutions involved over patient understanding. IC conversations are considered essential, as many patients do not thoroughly read IC documents. Conducting an unbiased IC conversation in an understandable manner may be challenging for physicians because they often have vested interests in recruiting patients for trials. Introducing evidence-based checklists for IC conversations and involving nursing staff may assist in addressing practical issues patients may have, reduce anxiety, and increase consent rates. Strategies to improve IC documents include reducing potentially irrelevant information (e.g., on contraception), improving the adaptation of international consent forms to local settings and incorporating graphical abstracts and study flowcharts to offer brief and visually engaging summaries. Additionally, fostering open dialogue and involvement of all relevant stakeholders (including patient representatives from various sociodemographic backgrounds) in designing IC documents may enhance IC processes. Many experts expressed the need for further research on the needs of different target groups, such as individuals with a migrant background or visual or other impairments. Conclusions There is a significant gap between legal and ethical obligations related to IC and patients not being able to understand the abundance of unfamiliar, complex information provided to them. Evidence-based IC checklists, involving nursing staff and improved written IC materials may help improve communication in this area. Further interventional research is required to IC processes in oncology with the aim to provide optimal, patient-centred care.
This study examines the evolution of medical education curricula globally, tracing the historical shift from traditional, experience-based training to contemporary, student-centered approaches with a particular focus on the Tikrit University College of Medicine (TUCOM) Iraq 1989 curriculum that implemented problem-based learning as a core strategy. It compares one of the core components of the curriculum, the small group discussions and the 7-jump learning strategy, to how they map Entrustable Professional Activities (EPAs) with a suggestion to integrate EPAs into the curriculum.
History of medicine. Medical expeditions, General works
Historically, medicine has been grounded in storytelling; however, contemporary practice has shifted toward expertise and empirical data, often neglecting the patient's narrative. This shift has created a gap in understanding the complexity of human suffering. Narrative medicine bridges this gap by prioritizing empathy, ethical sensitivity, and patient-centred care. It integrates patients’ life experiences and cultural backgrounds into clinical practice and aims to harmonize empirical methodologies with phenomenological insights. This review examines the theoretical foundations and practical applications of narrative medicine, particularly within medical ethics education. It highlights how narrative approaches improve moral reasoning, empathy, and cultural competence in healthcare professionals.Pedagogical methods such as reflective writing, attentive reading, and group discussions enhance ethical awareness and improve practitioners' capacity to manage complex clinical situations. Narrative medicine promotes a comprehensive understanding of illness and care by bridging the gap between evidence-based medicine and narrative approaches; it can also be integrated into education to address challenges such as cultural diversity, health inequalities, and ethical dilemmas arising from technological developments. However, ethical issues like power dynamics, privacy, and representation in patient narratives require careful management. Despite the existing challenges, narrative medicine offers a transformative framework for rethinking medical education and practice, ensuring that healthcare remains empathetic, equitable, and ethical.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
Veriserum is an open-source dataset designed to support the training of deep learning registration for dual-plane fluoroscopic analysis. It comprises approximately 110,000 X-ray images of 10 knee implant pair combinations (2 femur and 5 tibia implants) captured during 1,600 trials, incorporating poses associated with daily activities such as level gait and ramp descent. Each image is annotated with an automatically registered ground-truth pose, while 200 images include manually registered poses for benchmarking. Key features of Veriserum include dual-plane images and calibration tools. The dataset aims to support the development of applications such as 2D/3D image registration, image segmentation, X-ray distortion correction, and 3D reconstruction. Freely accessible, Veriserum aims to advance computer vision and medical imaging research by providing a reproducible benchmark for algorithm development and evaluation. The Veriserum dataset used in this study is publicly available via https://movement.ethz.ch/data-repository/veriserum.html, with the data stored at ETH Zürich Research Collections: https://doi.org/10.3929/ethz-b-000701146.
Recent studies indicate that robotic coaches can play a crucial role in promoting wellbeing. However, the real-world deployment of wellbeing robots raises numerous ethical and socio-technical questions and concerns. To explore these questions, we undertake a community-centered investigation to examine three different communities' perspectives on using robotic wellbeing coaches in real-world environments. We frame our work as an anticipatory ethical investigation, which we undertake to better inform the development of robotic technologies with communities' opinions, with the ultimate goal of aligning robot development with public interest. We conducted workshops with three communities who are under-represented in robotics development: 1) members of the public at a science festival, 2) women computer scientists at a conference, and 3) humanities researchers interested in history and philosophy of science. In the workshops, we collected qualitative data using the Social Robot Co-Design Canvas on Ethics. We analysed the collected qualitative data with Thematic Analysis, informed by notes taken during workshops. Through our analysis, we identify four themes regarding key ethical and socio-technical questions about the real-world use of wellbeing robots. We group participants' insights and discussions around these broad thematic questions, discuss them in light of state-of-the-art literature, and highlight areas for future investigation. Finally, we provide the four questions as a broad framework that roboticists can and should use during robotic development and deployment, in order to reflect on the ethics and socio-technical dimensions of their robotic applications, and to engage in dialogue with communities of robot users. The four questions are: 1) Is the robot safe and how can we know that?, 2) Who is the robot built for and with?, 3) Who owns the robot and the data?, and 4) Why a robot?.
Rucha Deshpande, Varun A. Kelkar, Dimitrios Gotsis
et al.
The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. The goal of this challenge was to promote the development of deep generative models (DGMs) for medical imaging and to emphasize the need for their domain-relevant assessment via the analysis of relevant image statistics. As part of this Grand Challenge, a training dataset was developed based on 3D anthropomorphic breast phantoms from the VICTRE virtual imaging toolbox. A two-stage evaluation procedure consisting of a preliminary check for memorization and image quality (based on the Frechet Inception distance (FID)), and a second stage evaluating the reproducibility of image statistics corresponding to domain-relevant radiomic features was developed. A summary measure was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, and to identify various artifacts. 58 submissions from 12 unique users were received for this Challenge. The top-ranked submission employed a conditional latent diffusion model, whereas the joint runners-up employed a generative adversarial network, followed by another network for image superresolution. We observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID-based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. This Grand Challenge highlighted the need for domain-specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.
Abdoul Jalil Djiberou Mahamadou, Aloysius Ochasi, Russ B. Altman
Data are essential in developing healthcare artificial intelligence (AI) systems. However, patient data collection, access, and use raise ethical concerns, including informed consent, data bias, data protection and privacy, data ownership, and benefit sharing. Various ethical frameworks have been proposed to ensure the ethical use of healthcare data and AI, however, these frameworks often align with Western cultural values, social norms, and institutional contexts emphasizing individual autonomy and well-being. Ethical guidelines must reflect political and cultural settings to account for cultural diversity, inclusivity, and historical factors such as colonialism. Thus, this paper discusses healthcare data ethics in the AI era in Africa from the Ubuntu philosophy perspective. It focuses on the contrast between individualistic and communitarian approaches to data ethics. The proposed framework could inform stakeholders, including AI developers, healthcare providers, the public, and policy-makers about healthcare data ethical usage in AI in Africa.
Abstract We discuss the concept of women’s empowerment in FemTech, considering cultural and legal differences, ethical concerns, and legal consequences. We claim that it is crucial to prioritize privacy, a fundamental right, especially in the case of changes in laws related to women’s health, such as Roe v. Wade in the US.
This article draws on the underexplored or novel accounts of inclusion and the moral accounts of decolonization in African health decolonial literature to increase our understanding of how ethics dumping manifests in health research partnerships, and what more ought to be done to eliminate this phenomenon. African decolonial health literature proposes “inclusion that matters” – conceptualized as substantial, respectful and deep engagement with African agency – as a solution to end domination or mitigate the “appearance” of inclusion. Based on this supposition, the harm of ethics dumping – and I demonstrate how – is that it fails to engage the agency of Africans, and listen to or echo their voices in health and health research collaborations on the continent, or research collaborations that have significant implications for them. This account of inclusion can usefully increase our thinking about ethics dumping, which is ultimately and in several ways a failure to practice responsible science. Research is required to increase our understanding of what could reasonably constitute responsible science from a variety of perspectives.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari
et al.
Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods.
An assurance case is a structured argument, typically produced by safety engineers, to communicate confidence that a critical or complex system, such as an aircraft, will be acceptably safe within its intended context. Assurance cases often inform third party approval of a system. One emerging proposition within the trustworthy AI and autonomous systems (AI/AS) research community is to use assurance cases to instil justified confidence that specific AI/AS will be ethically acceptable when operational in well-defined contexts. This paper substantially develops the proposition and makes it concrete. It brings together the assurance case methodology with a set of ethical principles to structure a principles-based ethics assurance argument pattern. The principles are justice, beneficence, non-maleficence, and respect for human autonomy, with the principle of transparency playing a supporting role. The argument pattern, shortened to the acronym PRAISE, is described. The objective of the proposed PRAISE argument pattern is to provide a reusable template for individual ethics assurance cases, by which engineers, developers, operators, or regulators could justify, communicate, or challenge a claim about the overall ethical acceptability of the use of a specific AI/AS in a given socio-technical context. We apply the pattern to the hypothetical use case of an autonomous robo-taxi service in a city centre.
Zachary A Stoebner, Daiwei Lu, Seok Hee Hong
et al.
Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process as possible. For the segmentation task, we adapted and analyzed three baseline deep learning models -- U-Net, U-Net++, and DenseNet -- to predict annotations on the frames of the endoscopic videos with the highest accuracy above 90\%. To show clinical potential for real-time use, we also confirmed that our best trained model can accurately annotate new videos at 30 frames per second. Our results demonstrate that the proposed method justifies continued development and study of image segmentation to annotate ureteroscopic video feeds.
The IHME Covid-19 prediction model has been one of the most influential Covid models in the United States. Early on, it received heavy criticism for understating the extent of the epidemic. I argue that this criticism was based on a misunderstanding of the model. The model was best interpreted not as attempting to forecast the actual course of the epidemic. Rather, it was attempting to make a conditional projection: telling us how the epidemic would unfold, given certain assumptions. This misunderstanding of the IHME’s model prevented the public from seeing how dire the model’s projections actually were.