ArticleBMJ health & care informatics2026
Self-regulating the use of large language models in clinical practice: a risk-stratified approach.
Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Grounded ophthalmic AI beyond the electronic health record trap: a critical narrative review.BMJ health & care informatics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid integration of large language models (LLMs) into clinical practice offers promising benefits, including assistance with documentation, decision support and patient communication. However, these advantages are tempered by concerns around model accuracy, data privacy, clinician trust and regulatory responsibility. In this comment, we propose a clinician-led, risk-stratified framework to guide responsible adoption of LLMs in healthcare. The framework categorises applications into four risk tiers: low risk, moderate risk, high risk and critical risk. Each tier demands tailored oversight, validation and governance, with increasing levels of statutory regulatory scrutiny (eg., European Union Artificial Intelligence Act and U.S. Food and Drug Administration) and clinical supervision. We argue that proactive self-regulation combined with ongoing quality management and clinician education is essential to safely integrate LLMs into ongoing care.
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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.