ArticleJournal of medical ethics2024
Trust criteria for artificial intelligence in health: normative and epistemic considerations.
Article in Journal of medical ethics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
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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
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ethical implications of high attrition in AI-based mental health interventions: a systematic review and meta-analysis.BMC medical ethics · 2026Pooled it
- Patient Concerns Regarding Artificial Intelligence Applications in Health Care: Systematic Review and Meta-Synthesis Based on Social Ecological Theory.Journal of medical Internet research · 2026Pooled it
- Trust and Technology Acceptance: Comparing Traditional Search Engines and Artificial Intelligence for Colorectal Cancer Information Seeking.Cancer control : journal of the Moffitt Cancer CenterTrial
- Explainability and Human Oversight for AI-Generated Exercise Guidance in Digital Healthcare: A Governance-Oriented Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Governing Generative AI for Healthy Ageing: A Normative Conceptual Framework for Societal Alignment, Epistemic Authority, and Value Convergence in Geriatric Care.Healthcare (Basel, Switzerland) · 2026Article
- Acceptable accuracy for medical AI: a survey of physicians and the general population in Sweden.BMJ health & care informatics · 2026Article
- Article
- Comments on "Forecasting anesthetic depth using an auto regressive transformer in propofol infusion during the induction phase" by Chi et al.Journal of anesthesia · 2026Article
- Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study.Journal of medical Internet research · 2026Article
- Between map and maze: reframing trust in healthcare AI.AI & society · 2026Review
- Is accuracy enough? trust and barriers to AI-based clinical decision support in clinical neurophysiology.Clinical neurophysiology practice · 2026Article
- Stakeholder Criteria for Trust in Artificial Intelligence-Based Computer Perception Tools in Health Care: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma.Clinical and experimental medicine · 2025Review
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Artificial intelligence, intellectual property, and human rights: mapping the legal landscape in European health systems.npj health systems · 2025Article
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- Advocating for trust in and trustworthy AI to transform evidence synthesis.Environmental evidence · 2025Article
- Stakeholder Perspectives on Early Feasibility Studies for Digital Health Technologies in the European Union: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- Integrating New Technologies in Lipidology: A Comprehensive Review.Journal of clinical medicine · 2025Review
- A multi-site study of clinician perspectives in the lifecycle of an algorithmic risk prediction tool.SSM. Qualitative research in health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is properly calibrated to a tool's computational capacities and limitations has both practical and ethical implications, given that overtrust or undertrust can influence over-reliance or under-reliance on algorithmic tools, with significant implications for patient safety and health outcomes. It is, thus, important to better understand how variability in trust criteria across stakeholders, settings, tools and use cases may influence approaches to using AI/ML tools in real settings. As part of a 5-year, multi-institutional Agency for Health Care Research and Quality-funded study, we identify trust criteria for a survival prediction algorithm intended to support clinical decision-making for left ventricular assist device therapy, using semistructured interviews (n=40) with patients and physicians, analysed via thematic analysis. Findings suggest that physicians and patients share similar empirical considerations for trust, which were primarily
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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.