Moral distress was first introduced by ethicist Andrew Jameton, and it has now been studied for more than four decades. Despite this long history, most existing research has rested on a largely unexamined assumption: that clinicians already recognize what they ought to do, and that the real problem begins only when external forces prevent them from doing it. Far less attention has been paid to a more fundamental question. How do healthcare professionals come to recognize that a situation is ethically significant in the first place? By overlooking this question, researchers have left the concept of moral distress somewhat blurry, difficult to distinguish from ordinary workplace stress, occupational burnout, disagreements about the appropriate course of care, or conflicts over professional roles and responsibilities.

A new study seeks to close that gap by returning to the philosophical foundations of the concept. Professor Tomohide Ibuki from the Institute of Arts and Sciences at Tokyo University of Science and Dr. Keiichiro Yamamoto from the Department of Clinical Research Management at the Center for Clinical Sciences, Japan Institute for Health Security, revisited moral distress from a philosophical perspective. Their paper, published online in the journal Nursing Ethics on July 9, 2026, draws on the work of the contemporary philosopher John McDowell to propose a new way of understanding how healthcare professionals develop moral sensitivity and why moral distress arises when that sensitivity collides with institutional reality.

The philosophical core of the paper rests on McDowell’s account of perception, second nature, and the space of reasons, including his discussion of Bildung, or ethical formation. On this view, moral sensitivity in healthcare is not an innate trait that some clinicians happen to possess and others lack. Instead, it is a cultivated capacity, developed through education and clinical experience, to perceive the ethically significant features of a clinical situation as reasons for action. Through years of training and practice, professionals learn to perceive a patient’s pain, a compromised sense of dignity, or an unmet need for care not as neutral clinical facts but as ethical reasons that call for a response. Perception itself, in this framework, is already moral.

This account allows the authors to philosophically reconstruct moral distress in a way that departs from the standard psychological picture. Rather than treating moral distress merely as stress or frustration at being blocked from acting, they describe it as a form of normative suffering. It occurs when a morally sensitive professional recognizes ethically significant reasons for action but is prevented by external constraints from responding to them. The distinction matters because it separates moral distress from general psychological distress. Ordinary stress can arise from workload, fatigue, or interpersonal conflict. Moral distress, by contrast, arises specifically when clinicians recognize ethical reasons for action but are unable to respond because of institutional or organizational constraints. The suffering is tied to the perceived ethical demand itself.

From this reconstruction flows one of the paper’s central and most striking claims, which the authors call the paradox of moral distress. As they explain, paradoxically, healthcare professionals with greater moral sensitivity may be more likely to recognize ethical problems and, under organizational and institutional constraints, more likely to experience moral distress. In other words, the clinicians who suffer most may not be those who are weakest or least able to cope, but those whose ethical perception is sharpest. Moral distress, on this reading, may sometimes be a sign of strong ethical awareness rather than personal weakness or an inability to handle the demands of the job.

The authors are careful to note that this relationship between sensitivity and distress is a conceptual proposal that remains to be examined empirically. The framework does not yet rest on data showing that more morally sensitive clinicians report more distress, and future studies will be needed to test whether the paradox holds in practice. Even so, the proposal offers a clearer foundation for that research than the concept has previously enjoyed. By specifying how moral sensitivity is formed and how it relates to the experience of being constrained, the framework gives researchers a more precise target for empirical investigation, survey design, and intervention studies across different clinical settings.

If supported by future research, the work could reshape how healthcare organizations approach the problem. Rather than focusing only on helping individuals become more resilient or better able to cope with stress, healthcare systems should also examine whether clinicians are given the opportunity, resources, and institutional support to act on the ethical concerns they identify. The authors argue that hospitals should combine ethics education with organizational support, including opportunities for ethical discussion, accessible ethics consultation, responsive leadership, adequate staffing and resources, and decision-making processes that ensure clinicians’ ethical concerns are heard and addressed. On this view, an organization that treats moral distress purely as an individual wellness issue risks missing the structural conditions that produce it.

The implications for ethics education are equally significant. If moral sensitivity is a cultivated capacity, then education should deliberately foster the ability to recognize ethically significant situations. But the authors emphasize that such education must be paired with organizational cultures that encourage ethical discussion and shared decision-making. Training clinicians to see ethical reasons for action while leaving them powerless to respond would, on the framework’s own logic, deepen rather than relieve the problem. Education and institutional reform therefore need to advance together, so that the capacity for ethical perception is matched by genuine opportunities to act on it.

Ultimately, the researchers hope their work will encourage healthcare institutions to rethink moral distress and to see it as a signal that organizational conditions may be standing in the way of ethical care. As they put it, their framework could help create workplaces where healthcare professionals can more readily express ethical concerns and could inform educational and organizational improvements that support better patient care. The authors also suggest that addressing moral distress requires greater attention to organizational ethics and institutional reforms, rather than relying solely on individual coping strategies. If the paradox they propose proves correct, the most ethically attuned members of a healthcare team may be its most valuable early-warning system, and listening to them may be one of the most effective ways to protect both clinician well-being and the quality of patient care.

Subject of Research: A philosophical reconstruction of moral distress and moral sensitivity in healthcare practice

Article Title: New philosophical framework reframes moral distress in healthcare

Article References: New philosophical framework reframes moral distress in healthcare. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: moral distress, healthcare ethics, nursing ethics, moral sensitivity, John McDowell, burnout, clinician well-being, organizational culture, ethics education, patient care, normative suffering, Tokyo University of Science

The study, published in the journal AI & Society by Ivan Efreaim Gozum of the University of Santo Tomas in Manila, examines how AI-assisted triage systems allocate scarce medical resources during public health emergencies and in ordinary emergency department settings. Gozum’s central claim is provocative: contemporary triage frameworks, even those that employ sophisticated multidimensional clinical criteria beyond the widely used SOFA scores, remain ethically incomplete. They address the calculable dimensions of patient care while leaving the moral and relational dimensions of clinical judgment underdeveloped, and AI systems inherit and amplify that incompleteness.

The technical context matters here. Triage scoring systems such as the Sequential Organ Failure Assessment, or SOFA, score were developed to predict mortality in critically ill patients by quantifying dysfunction across organ systems. During the COVID-19 pandemic, such scores became central to allocation protocols for intensive care beds and ventilators, and researchers have since validated their predictive value across diverse patient populations, including patients with and without COVID-19 infection. More recently, machine learning models have been developed to predict critical outcomes in emergency departments, and systematic reviews document a rapid expansion of AI-based triage tools into clinical practice worldwide.

Yet the empirical record on these tools is sobering. A landmark study published in Science in 2019 dissected racial bias in a widely deployed algorithm used to manage the health of populations, showing how a seemingly neutral proxy variable led the system to systematically underestimate the illness of Black patients. Reviews of prediction models developed during the pandemic found that most were at high risk of bias and few were suitable for clinical use. Studies of emergency department professionals’ experiences with machine learning triage integration reveal persistent concerns about opacity, trust, and the erosion of clinical autonomy. Gozum’s analysis takes these documented problems as its starting point: algorithmic bias, opacity, and the reduction of persons to calculable outcomes are not edge cases but structural features of how triage AI is currently conceived.

What would Aquinas add to this picture? Gozum’s framework draws on the virtue ethics tradition that Aquinas inherited from Aristotle and systematized in the Summa Theologica. Virtue ethics differs fundamentally from the utilitarian and deontological frameworks that dominate mainstream bioethics. Where utilitarian approaches, which gained prominence during pandemic allocation debates, ask which policy maximizes overall benefit, and Kantian approaches ask which rules respect persons as ends in themselves, virtue ethics asks a prior question: what kind of moral agent does good care require, and what habits of character enable that agent to judge well in particular, concrete circumstances?

Gozum develops this Thomistic framework across three ethical levels. The first concerns AI system design itself. For Aquinas, justice is the virtue that gives each person their due, and Gozum argues that a triage algorithm designed without attention to justice, including fairness across social groups and the dignity of patients who cannot be captured by a score, fails at the level of its architecture. The second level concerns the moral agency of healthcare practitioners. Prudence, in the Thomistic sense, is not mere caution but the perfected ability to deliberate well about particular actions, and Gozum contends that clinicians using AI triage must exercise precisely this practical wisdom, treating the algorithm’s output as one input among many rather than as a verdict. The third level concerns transparency and the conditions for responsible human oversight, since oversight is impossible when the reasoning behind a triage recommendation cannot be inspected, questioned, or overridden.

Crucially, the framework positions AI as an assistive instrument rather than a replacement for ethical discernment. This distinction echoes arguments from the philosophy of technology about the technological imperative, the tendency to adopt and extend a technology simply because it exists and works. Gozum’s Thomistic response is that the moral worth of an action lies in the free, informed choice of the agent, not in the efficiency of the tool. A triage system that outputs rankings faster than any human committee still does not make a moral decision; it makes a statistical estimate, and the moral decision remains with the clinician and the institution that act on it. Charity, the third virtue in Gozum’s framework, grounds this relational view: care is not the distribution of resources to outcomes but a relationship between persons, and systems that obscure that relationship, however accurate their predictions, diminish the practice of medicine.

The study situates itself within ongoing bioethical debates about resource allocation that intensified during COVID-19. Influential frameworks published in the New England Journal of Medicine in 2020 proposed maximizing benefits, treating people equally, and prioritizing the worst off as core values for scarce resource allocation. Other scholars defended explicitly utilitarian approaches for the pandemic, while disability rights scholars and ethicists raised alarms about quality-of-life judgments that could systematically disadvantage disabled patients. Gozum does not reject these frameworks outright, but argues that they insufficiently address the moral and relational dimensions of clinical judgment, and that a virtue-centered account can complement them by specifying who must judge, how they must be formed, and what institutional conditions make good judgment possible.

The practical implications are concrete. At the level of governance, Gozum calls for an interdisciplinary framework integrating theology, philosophy, bioethics, and healthcare governance, an unusual combination in AI policy circles but one that reflects the breadth of existing AI ethics guidance, which a major 2019 review found to converge on principles of transparency, justice, non-maleficence, responsibility, and privacy without specifying how to enact them. Virtue ethics offers an enactment mechanism: institutions can cultivate prudence in clinicians through training that treats AI literacy as a moral skill, demand justice in procurement by auditing algorithms for disparate performance across patient groups, and require charity, in the sense of relational care, by ensuring that no patient’s care is reduced to an automated score. The approach also builds on Gozum’s earlier work connecting Catholic social teaching with AI ethics to address inequity in AI healthcare and on philosophical work applying Thomistic risk analysis to social welfare.

Whether a medieval theology of virtue can meaningfully discipline twenty-first-century machine learning remains an open question, and skeptics will note that virtue formation is slow where algorithmic deployment is fast. But the study’s timing is pointed. AI triage systems are moving from pilot studies to routine use in emergency departments, and the documented failures of biased and opaque algorithms suggest that technical fixes alone have not resolved the ethical core of the problem. Gozum’s argument is that the core was never technical in the first place. Deciding who is treated first in an emergency is an act of practical wisdom performed by moral agents accountable to the people they serve, and any system, however intelligent, that displaces that act also displaces the responsibility and the dignity that come with it. In that sense, the oldest ethical tradition in the conversation may have the newest thing to say.

Subject of Research: Thomistic virtue ethics as a framework for the ethical use of AI triage systems in healthcare emergencies

Article Title: Aquinas and the ethics of AI triage systems in healthcare emergencies

Article References: Gozum, I. E. (2026). Aquinas and the ethics of AI triage systems in healthcare emergencies. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03225-0

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03225-0

Keywords: artificial intelligence, triage, healthcare ethics, Thomas Aquinas, virtue ethics, emergency medicine, algorithmic bias, resource allocation, medical ethics, AI & Society, human dignity, clinical decision-making

The study, authored by Rosa E. Martín-Peña of Leibniz University Hannover and Zhengmin Li of the University of Copenhagen, challenges the dominant assumption in medical AI ethics that frameworks built on autonomy, individual rights and data protection can simply be applied worldwide. Global reviews of AI ethics documents have identified a stable “consensus package” of principles drafted mainly in Europe and North America, now embedded in guidance from the World Health Organization and the OECD. The authors argue that this dominance makes it easy to treat assumptions about personhood and harm as universal, when in fact what counts as harm, care, or proper human–machine interaction depends on local moral traditions, institutional arrangements and regulatory histories.

To make the comparison rigorous, the researchers developed a triadic analytical framework examining three dimensions in each jurisdiction: moral anthropologies, or how persons and harms are conceptualised; models of legitimacy, or who is authorised to define risk; and regulatory instruments, spanning the continuum from soft law to hard law. Japan, China and Hong Kong were selected as a most-different-systems design, representing soft-law cooperative governance, centralised hard-law architecture, and a common-law rights-based framework respectively. The payoff of this comparison is a fourth, unexpected axis: each jurisdiction embodies a distinct temporal logic of harm prevention—iterative in Japan, preventive in China, and reactive in Hong Kong.

Japan constructs the patient as a relational node within family and community rather than a bounded individual. This anthropology draws on Confucian role ethics, the philosopher Watsuji Tetsurō’s concept of ningen as a dialectical unity of individual and social existence, and the value of wa, or harmony, which appears explicitly in Japanese AI governance documents such as the Social Principles of Human-Centric AI. Legitimacy is technico-consensual, deriving from consensus among experts, industry and government in a pattern consistent with the nemawashi tradition of building agreement before formal decisions. Accountability is diffuse, spread across developers, providers, families and the state, and coordinated primarily through non-binding guidelines rather than enforceable mandates.

The temporal consequence is striking. Japan’s governance rationality trusts norms to emerge through use and professional dialogue, stipulating no harm-prevention requirements in advance of deployment. PARO itself, despite receiving US Food and Drug Administration certification as a Class II biofeedback medical device in 2009, operates in Japan without equivalent classification. Companion robots fall outside the Software as a Medical Device framework, so adverse relational outcomes—dependency formation, displacement of family contact, caregiver burden—are not systematically collected under any regulatory channel. Ethnographic research in Japanese care facilities documented one resident who became so attached to PARO that she refused to eat or sleep without it, a case of dependency that existing soft-law instruments do not directly address. The structural risk, the authors note, is that commercial deployment outpaces the norm-building process on which the model depends.

China takes the opposite approach, framing emotional dependency as a population health risk requiring preventive state intervention. The patient is constructed as a protected subject, and legitimacy derives from the Party-state’s role as guarantor of collective welfare and social stability. Hard-law instruments are layered densely: the Personal Information Protection Law and Data Security Law of 2021, National Medical Products Administration guidelines for medical AI, and sector-specific measures on algorithmic recommendation, deep synthesis and generative AI. The centrepiece is the Interim Measures for the Administration of Anthropomorphic AI Interactive Services, finalised in April 2026 and effective from 15 July 2026, among the most explicit attempts globally to regulate emotional AI directly. Key provisions include two-hour usage reminders to prevent dependency, restrictions on training models with interaction data, mandatory emergency contact mechanisms for elderly users, and a ban on AI systems simulating family relationships—a provision the authors read as consistent with Confucian role ethics, even though official commentary frames it in people-centred vocabulary.

This preventive architecture carries its own trade-offs. A system like PARO entering the Chinese market would face exhaustive ex-ante review across multiple regulatory regimes, yet a patient who developed problematic dependency would have no institutionalised avenue for contestation. The authors describe the result as mis-regulation rather than over-regulation: exhaustive procedural coverage of identifiable technical risks alongside a structural absence of frameworks for relational and emotional harms. Patients are protected yet not necessarily empowered, with administrative risk absorption substituting for individual redress. Enforcement practice illustrates the logic: in April 2026, the Cyberspace Administration of China took action against three prominent generative AI platforms for failing to implement mandatory content labelling, reflecting broader regulatory concern with emotional dependency and manipulation.

Hong Kong, by contrast, constructs the patient as a rights-holder with autonomy over health decisions and personal data, governed through a juridico-procedural rationality that has become constitutionally unsettled since 2020. The Personal Data (Privacy) Ordinance provides the binding baseline, imposing consent, purpose limitation and data security obligations, while a suite of non-binding instruments—the Privacy Commissioner’s ethical AI guidance and the 2024 Model Personal Data Protection Framework—translates principles into operational expectations. A 2025 compliance check of sixty organisations found no contravention of the Ordinance, and roughly 63 percent of organisations collecting personal data through AI referenced the Model Framework—but adoption remained voluntary. The temporal logic is reactive: violations are adjudicated only after they occur. A robot found to collect biometric data beyond its stated purpose would face regulatory action; a robot that fostered unhealthy emotional dependency or displaced family contact in a cognitively vulnerable resident would not, because those harms fall structurally outside the privacy perimeter.

The comparison exposes blind spots that no single jurisdiction escapes. Psychological dependency, relational disruption and the erosion of human empathy fall outside traditional regulatory categories in all three cases, differing only in which gaps are structural and which are contingent. The problem extends beyond Asia: the EU AI Act prohibits manipulative systems and those exploiting vulnerabilities, yet the European Commission’s own implementation guidelines suggest that a system detecting users’ moods and recommending emotionally aligned content may be permissible if it causes no significant harm—leaving the boundary between therapeutic care and prohibited manipulation indeterminate. The authors also warn of a potential “Beijing Effect”: because developers seeking access to the Chinese market must comply with usage limits and the family-simulation ban, these requirements are likely to reshape global product design, producing de facto convergence on China’s construction of emotional harm without any democratic deliberation about whose ontology of emotion should govern.

The study’s prescription is not harmonisation but epistemological humility: recognition that different traditions construct emotion, personhood and harm differently, and that no single construction has privileged access to truth. The authors propose concrete mechanisms, including disclosure of the governance rationality shaping product design through ISO/IEC standardisation processes, the UN Global Digital Compact’s review mechanisms, and bilateral regulatory dialogues such as the EU–Japan Digital Partnership. Such transparency would not resolve incompatibilities, but it would make them legible. As the authors conclude, governance without ontological awareness risks regulating technologies while misunderstanding the kinds of patients they are said to protect—and international frameworks will otherwise continue to govern a universal patient who does not exist.

Subject of Research: Comparative governance of emotional AI in healthcare across Japan, China and Hong Kong

Article Title: No universal patient: temporal logics of harm prevention in emotional AI healthcare across Japan, China, and Hong Kong

Article References: Martín-Peña, R. E., & Li, Z. (2026). No universal patient: temporal logics of harm prevention in emotional AI healthcare across Japan, China, and Hong Kong. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03355-5

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03355-5

Keywords: emotional AI, companion robots, PARO, AI governance, healthcare ethics, Japan, China, Hong Kong, harm prevention, temporal logics, Confucian ethics, data protection