CIOMS cited

The aim of this paper is to discuss the ethical and legal justification of the broadly accepted ethical principle that researchers may not include adults without decision-making capacity to consent to participating in research studies if there are potential research participants with capacity who could be included instead. From the perspective of disability rights, the paper argues that this ethical principle is overprotective in its current form as it gives insufficient consideration to providing persons with disabilities the opportunity to participate in research on an equal basis with others. Nonetheless, the paper also argues that rather than rejecting this ethical principle entirely, it should be qualified to strike a better balance between protecting people without decisional capacity to consent to research and including them.

This response analyses the discussion by Parsa-Parsi et al on the 2024 revision of the Declaration of Helsinki (DoH). The author argues that they underestimate the conceptual and practical significance of the new approach to the concept of vulnerability and how this approach connects to the Council for International Organizations of Medical Sciences (CIOMS) 2016 Guidelines. Drawing on the idea of ‘layers of vulnerability’, the author highlights how vulnerability is often contextual, dynamic and cumulative, and how this view avoids essentialising entire groups (such as pregnant persons) while clarifying when and how people facing multiple vulnerabilities can be ethically included in research with appropriate protections. The author further argues that there are similarities between DoH and CIOMS and that a closer alignment with CIOMS in the interpretation of the DoH offers a more coherent and practically useful framework for research ethics committees and researchers.

Migraine is one of the most disabling neurological disorders worldwide and is a leading cause of years lived with disability. The introduction of calcitonin gene-related peptide (CGRP)-directed monoclonal antibodies and gepants has moved prevention from a largely empirical, symptom-management paradigm toward mechanism-based therapy that can plausibly alter disease trajectory. That therapeutic shift raises a decisive neuroethical question: can it still be ethically acceptable to randomize participants with frequent migraine to months of placebo when highly effective, migraine-specific preventive options exist?  

International ethics frameworks emphasize both the protection of vulnerable participants and the importance of equitable inclusion in research. In ultra-rare gene therapy trials, extensive safety monitoring, long-term follow-up requirements, and specialized treatment infrastructure are necessary to ensure participant protection and scientific validity. However, these requirements operate within healthcare systems characterized by geographic, financial, and social inequalities, raising concerns about whether all eligible patients have a fair opportunity to participate. Recent regulatory reforms in the European Union, United Kingdom and United States increasingly promote methodological flexibility for small patient populations through adaptive trial designs, risk proportionate oversight, and alternative evidentiary approaches.

The authors identify five areas in which AI tools can improve clinical trial recruitment: generation and optimization of trial eligibility criteria, clinical trial matching, clinical trial enrichment, digital twins and synthetic data, and informed consent. They argue for a sixth domain of AI application, namely the calculation of payment for research participants.

Women make up nearly half of all people living with cardiovascular disease, yet they remain underrepresented in the trials that set diagnostic thresholds, dosing regimens, and treatment algorithms. This narrative review, built on a targeted search of PubMed, the JAMA Network, and regulatory databases through July 2026, examines that gap through a bioethical lens rather than an epidemiological one. Existing state-of-the-art reviews have documented the scale of underrepresentation in detail. What has not been done systematically is testing that evidence against the four principles of biomedical ethics (autonomy, beneficence, nonmaleficence, and justice), and extending the same framework to the algorithmic tools now being trained on decades of sex-imbalanced data. That persistent underrepresentation, where it lacks scientific justification, constitutes a multidimensional bioethical problem rather than a purely methodological one. This article reviews the biological, structural, and regulatory drivers of the gap, the pharmacokinetic and pharmacodynamic consequences of sex-based evidence deficits, the regulatory arc from the FDA’s 1977 exclusion guidance to the 2024 Diversity Action Plans, and the emerging risk that AI-driven cardiovascular tools, trained on that same imbalanced evidence, could encode and scale it further.

Artificial intelligence (AI) is increasingly being explored to support pharmacovigilance activities including processes involving individual case safety reports. These technologies may improve efficiency and enable new analytical capabilities, but they also raise important questions regarding the responsible use of these systems in contexts that directly support regulatory decision-making and patient safety. In response to these opportunities and challenges, the Council for International Organizations of Medical Sciences (CIOMS) set up Working Group XIV on Artificial Intelligence in Pharmacovigilance to establish and promote principles and guidance for this area. The resulting CIOMS report was published in December 2025 and presents seven guiding principles: risk-based approach, human oversight, validity and robustness, transparency, data privacy, fairness and equity, and governance and accountability. To maximise stakeholder awareness and understanding, this article provides an overview of the CIOMS XIV guidance and illustrates how these principles may be applied in practice through selected use cases. The report aims to support stakeholders in developing and deploying AI systems in a manner that maintains scientific rigor, transparency, and public trust while enabling innovation to support safe use of medicines worldwide.