ArticleEthics and information technology2023
Automated opioid risk scores: a case for machine learning-induced epistemic injustice in healthcare.
Article in Ethics and information technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Algorithmic gaslighting in healthcare: mechanisms, harms, and epistemic injustice in context.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- Artificial Intelligence-Enabled Prescribing in Opioid Use Disorder Care: Decision Support, Risk Scores, Large Language Model Copilots, and Digital Therapeutics (2024-2025).European addiction research · 2026Review
- Vigilant Algorithms and the Contagion of Risk in Automated Opioid Risk Assessment.Sociology of health & illness · 2026Article
- A Portable, Patient-Possessed Health Record: Architecture for Care Coordination as an Alternative to Centralized Data Aggregation.Pharmacy (Basel, Switzerland) · 2026Article
- Responsible AI for safer opioid risk management in older adults.npj health systems · 2026Article
- Why we believe chatbots: trust calibration as a design problem.Frontiers in psychology · 2026Article
- Temporal Aspects of Epistemic Injustice: The Case of Patients with Drug Dependence.Journal of bioethical inquiry · 2025Article
- The Rise of Clinical Decision Support Algorithms in Pain Management 2009-2024.Journal of general internal medicine · 2025Article
- The need for epistemic humility in AI-assisted pain assessment.Medicine, health care, and philosophy · 2025Article
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Keeping an AI on the mental health of vulnerable populations: reflections on the potential for participatory injustice.AI and ethics · 2025Article
- Principles and Policy Recommendations for Comprehensive Genetic Data Governance.Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · 2025Article
- Opportunities for incorporating intersectionality into biomedical informatics.Journal of biomedical informatics · 2024Article
- JAMIA at 30: looking back and forward.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients' likelihood of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems' decision-making processes can be captured through the lenses of Miranda Fricker's account of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.