ArticleJournal of the American Medical Informatics Association : JAMIA2021
Enhancing trust in AI through industry self-governance.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- A Health Informatics System in the South Australian Public Health Network: Implementation Report.JMIR medical informatics · 2026Article
- Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks.NPJ digital medicine · 2026Article
- Advancing healthcare AI governance through a comprehensive maturity model based on systematic review.NPJ digital medicine · 2026Article
- Expectations of healthcare AI and the role of trust: understanding patient views on how AI will impact cost, access, and patient-provider relationships.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.BMC medical informatics and decision making · 2024Review
- Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities.Pharmaceutical medicine · 2024Review
- Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context.Asian bioethics review · 2024Article
- Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk.Regulation & governance · 2024Article
- How patients distinguish between clinical and administrative predictive models in health care.The American journal of managed care · 2024Article
- Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.JAMA network open · 2023Article
- The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review.Interactive journal of medical research · 2023Article
- Implementing AI in healthcare-the relevance of trust: a scoping review.Frontiers in health services · 2023Article
- Article
- Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.BMJ health & care informatics · 2022Review
- Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science?PET clinics · 2021Review
- Primary Care Provider Preferences Regarding Artificial Intelligence in Point-of-Care Cancer Screening.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is critical to harnessing value from exponentially growing health and healthcare data. Expectations are high for AI solutions to effectively address current health challenges. However, there have been prior periods of enthusiasm for AI followed by periods of disillusionment, reduced investments, and progress, known as "AI Winters." We are now at risk of another AI Winter in health/healthcare due to increasing publicity of AI solutions that are not representing touted breakthroughs, and thereby decreasing trust of users in AI. In this article, we first highlight recently published literature on AI risks and mitigation strategies that would be relevant for groups considering designing, implementing, and promoting self-governance. We then describe a process for how a diverse group of stakeholders could develop and define standards for promoting trust, as well as AI risk-mitigating practices through greater industry self-governance. We also describe how adherence to such standards could be verified, specifically through certification/accreditation. Self-governance could be encouraged by governments to complement existing regulatory schema or legislative efforts to mitigate AI risks. Greater adoption of industry self-governance could fill a critical gap to construct a more comprehensive approach to the governance of AI solutions than US legislation/regulations currently encompass. In this more comprehensive approach, AI developers, AI users, and government/legislators all have critical roles to play to advance practices that maintain trust in AI and prevent another AI Winter.
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What OpenQuestion holds
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.