ArticleFrontiers in digital health2024
The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation.
Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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Who cites it
11 citing papers in PubMed.
- Triage and workflow optimization with artificial intelligence in pediatric imaging.Pediatric radiology · 2026Review
- Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions.Journal of clinical medicine · 2026Review
- Artificial Intelligence Virtual Organoids (AIVOs).Bioactive materials · 2026Review
- Psychotherapists' Trust, Distrust, and Generative AI Practices in Psychotherapy: Qualitative Study.Journal of medical Internet research · 2026Article
- Clarifying validation terminologies in healthcare.NPJ digital medicine · 2026Article
- An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework.PloS one · 2026Article
- Between map and maze: reframing trust in healthcare AI.AI & society · 2026Review
- Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis.NPJ digital medicine · 2025Article
- Rethinking AI Workflows: Guidelines for Scientific Evaluation in Digital Health Companies.JMIR AI · 2025Article
- Population health management genomic new-born screens and multi-omics intercepts.Frontiers in artificial intelligence · 2024Review
- Research hotspots and trends of artificial intelligence ethics in medicine: A bibliometric analysis from 1999 to 2025.Digital healthReview
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) has the potential to revolutionize healthcare, for example via decision support systems, computer vision approaches, or AI-based prevention tools. Initial results from AI applications in healthcare show promise but are rarely translated into clinical practice successfully and ethically. This occurs despite an abundance of "Trustworthy AI" guidelines. How can we explain the translational gaps of AI in healthcare? This paper offers a fresh perspective on this problem, showing that failing translation of healthcare AI markedly arises from a lack of an operational definition of "trust" and "trustworthiness". This leads to (a) unintentional misuse concerning what trust (worthiness) is and (b) the risk of intentional abuse by industry stakeholders engaging in ethics washing. By pointing out these issues, we aim to highlight the obstacles that hinder translation of Trustworthy medical AI to practice and prevent it from fulfilling its unmet promises.
Indexed as
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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.