Trial reportNature medicine2026
A large language model for complex cardiology care.
Trial report in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 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
- Artificial intelligence in cardiology: an evidence map across subspecialties, 2014-26.European heart journal. Digital health · 2026Article
- Prospective evidence for conversational medical AI is hard, but non-negotiable.Nature medicine · 2026Article
- Artificial intelligence in orthopaedics should be built with surgeons, not just for them!Journal of experimental orthopaedics · 2026Article
- Process-Oriented, Behaviorally Anchored Assessment of Clinical Reasoning in Large Language Models and the Effect of Extended Thinking: Protocol for a Prospective, Multigroup, Comparative Study.JMIR research protocols · 2026Article
- Large Language Model Recommendations for Heart Failure Therapy.Journal of cardiac failure · 2026Article
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Article
- General-purpose large language models outperform specialized clinical AI tools on medical benchmarks.Nature medicine · 2026Article
- Artificial stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.European heart journal. Digital health · 2026Article
- Digital heart initiative: an ecosystem for digital discovery and precision medicine in cardiology.National science review · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The effects of multitype prompt engineering for large language models in hypertension treatment decisions.NPJ digital medicine · 2026Article
- Intention to Use Large Language Models Among Clinical Nurses in China With Prior Familiarity With or Experience Using LLMs: A Qualitative Study.Journal of nursing management · 2026Article
- The road from standardized to personalized medicine: the difficult-to-treat framework as a critical waystation.Frontiers in medicine · 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
34 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology, where timely, accurate management determines outcomes. We explored the potential of Articulate Medical Intelligence Explorer (AMIE), a large language model-based experimental medical artificial intelligence system, to augment clinical decision-making in this challenging context. We conducted a randomized controlled trial comparing large language model-assisted care with the usual care of complex patients suspected of having a genetic cardiomyopathy, and we curated a real-world dataset of complex cases from a subspecialist cardiology practice. Nine participating general cardiologists were provided with access to both clinical text reports and raw diagnostic data-including electrocardiograms, echocardiograms, cardiac magnetic resonance imaging scans and cardiopulmonary exercise testing-and were randomized to manage these cases, either with or without assistance from AMIE. We developed a ten-domain evaluation rubric used by three blinded subspecialists to evaluate the quality of triage, diagnosis and management. In our randomized controlled trial with retrospective patient data, subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie. Overall, subspecialists preferred AMIE-assisted cardiology assessments 46.7% of the time, compared with 32.7% for cardiologists alone (P = 0.02), with 20.6% rated as a tie. Subspecialists also quantified errors, extra and missing content, reasoning and potential bias. Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033) and more missing content (37.4% versus 17.8%, P = 0.0021) than cardiologists assisted by AMIE. Lastly, cardiologists who used AMIE reported that AMIE helped their assessment more than half the time (57.0%) and saved time in 50.5% of cases.
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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.