When emergency departments are overwhelmed and ventilators run short, hospitals increasingly turn to artificial intelligence to decide who gets treated first. These algorithmic triage systems promise speed, consistency, and data-driven objectivity in moments when human clinicians are stretched beyond their limits. But a new study argues that the deepest questions raised by AI triage are not technical at all, and that the answers may lie with an unlikely advisor: Thomas Aquinas, the thirteenth-century Dominican theologian whose writings on virtue and moral reasoning have found fresh relevance in the age of machine learning.
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
News Source: Blake Davidson. (October 8, 2026). A 13th-Century Theologian Offers a Surprising Fix for AI Triage Ethics. Scienmag.
