ArticleFrontiers in digital health2024
A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare.
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 57 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
57 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Ethical concerns and strategies for implementing artificial intelligence in healthcare: a review of empirical studies.BMC medical ethics · 2026Pooled 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
- The scientific evidence of commercial AI products for MRI acceleration: a systematic review.European radiology · 2025Pooled it
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey.Frontiers in digital health · 2025Pooled it
- Challenges and Recommendations in Regulating AI Medical Devices in the European Union: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI.PLOS digital health · 2026Article
- The economic imperative for an AI Productivity Index in healthcare: Beyond clinical promise to fiscal accountability.Clinical medicine (London, England) · 2026Article
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Digital diagnostics, biomarkers and therapeutics in an evolving healthcare system: From promise to practice.British journal of clinical pharmacology · 2026Review
- Evaluating patients' trust in health information based on different dimensions of trust.Scientific reports · 2026Article
- Parents' knowledge, attitudes, and concerns regarding the use of artificial intelligence in pediatric medicine: a cross-sectional study.BMC pediatrics · 2026Article
- The 'Hippocratic Oath' for AI-based clinical decision support systems.BMC medical informatics and decision making · 2026Article
- Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study.JMIR human factors · 2026Article
- Global Sensitivity Analysis for Robust XAI: Quantifying Clinical Risk and Prediction Instability in Dermoscopic Image Classification.Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- Prediction model for additional procedure requirement in flexible ureterorenoscopy using explainable artificial intelligence.Scientific reports · 2026Article
- Understanding Clinicians' Informational Needs for AI-Driven Clinical Decision Support Systems: Qualitative Interview Study.JMIR medical education · 2026Article
- Important Ethical, Technical, and Epidemiological Considerations in an AI Tool Production (ETEPAI): Scoping Review.JMIR AI · 2026Review
- AID-RT: Standardising Artificial Intelligence Documentation in RadioTherapy with a domain-specific model card.Physics and imaging in radiation oncology · 2026Review
- Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study.Journal of medical Internet research · 2026Article
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
Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was "unavailable", "partially available," or "fully available." The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.
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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.