ArticleEuropean heart journal2024
Artificial intelligence: revolutionizing cardiology with large language models.
Article in European heart journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
- Large Language Models in Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- Comparative evaluation of artificial intelligence-assisted literature search tools for identifying clinically meaningful evidence in cardiology.European heart journal. Digital health · 2026Article
- Large language models approach clinician performance in ESC cardiovascular risk stratification: a vignette-based benchmark study.European heart journal. Digital health · 2026Article
- [Artificial intelligence in internal medicine exemplified by cardiology].Innere Medizin (Heidelberg, Germany) · 2026Review
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
- Article
- Artificial intelligence adoption in French cardiovascular care: a multiprofessional survey of barriers and facilitators.European heart journal. Digital health · 2026Article
- Innovations In Artificial Intelligence-Guided Atherosclerosis Cardiovascular Imaging: From Detection to Prognosis.Current atherosclerosis reports · 2026Review
- Diagnostic and Therapeutic Options in Myocarditis and Inflammatory Cardiomyopathy.Biomedicines · 2026Review
- Digital Educational Strategies to Implement Evidence-Based Care for Atherosclerotic Cardiovascular Disease.Current atherosclerosis reports · 2026Review
- Cardiology knowledge assessment of retrieval-augmented open versus proprietary large language models.PLOS digital health · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Reimagining cardiac care with AI, LLMs, blockchain, and metaverse.Global cardiology science & practice · 2026Review
- Artificial intelligence in cardiology: a narrative review with focus on patient outcomes.Cardiovascular diagnosis and therapy · 2026Review
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios.Medical sciences (Basel, Switzerland) · 2026Article
- Comparative study of the performance of ChatGPT-4, Claude, Gemini, Mistral, and perplexity on multiple-choice questions in cardiology.BMC cardiovascular disorders · 2025Article
- Exploring the potential of large language models for assessing medication adherence to the ESC heart failure guidelines.JAMIA open · 2025Article
- A multi-query, multimodal, receiver-augmented solution to extract contemporary cardiology guideline information using large language models.European heart journal. Digital health · 2025Article
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 at 3 institutions in 3 countries.
Funding
Abstract
Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.
Indexed as
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.