ReviewNPJ digital medicine2025
Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Article
- Sustaining High Reliability Amid Artificial Intelligence Adoption in Oncology.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Reporting Quality of Large Language Model Studies: A Cross-Sectional Audit of High-Ranking Radiology and Medical Imaging Journals.Korean journal of radiology · 2026Article
- Open-weight language models for procedural impression generation.European radiology · 2026Article
- The ethics of listening walls: patient autonomy and consent in the age of ambient clinical AI.NPJ digital medicine · 2026Article
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Seven deadly sins in artificial intelligence for digital medicine.NPJ digital medicine · 2026Article
- Article
- Artificial intelligence in cardiology: a narrative review with focus on patient outcomes.Cardiovascular diagnosis and therapy · 2026Review
- An LLM-assisted framework for accelerated and verifiable clinical hypothesis testing from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Beyond the algorithm: embedding ethics for trustworthy AI in radiology and oncology.Frontiers in digital health · 2026Article
- Real-world application of large language models for automated TNM staging using unstructured gynecologic oncology reports.NPJ precision oncology · 2025Article
- The perils of politeness: how large language models may amplify medical misinformation.NPJ digital medicine · 2025Article
- Perceptions of Artificial Intelligence in Medical Documentation: A Cross-Sectional Study of Healthcare Professionals.Cureus · 2025Article
- Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline.Journal of medical Internet research · 2025Review
- Designing AI tools to advance health equity in resource-constrained low- and middle-income countries.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The integration of large language models (LLMs) into electronic health records offers potential benefits but raises significant ethical, legal, and operational concerns, including unconsented data use, lack of governance, and AI-related malpractice accountability. Sycophancy, feedback loop bias, and data reuse risk amplifying errors without proper oversight. To safeguard patients, especially the vulnerable, clinicians must advocate for patient-centered education, ethical practices, and robust oversight to prevent harm.
Identifiers
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.