ArticleEuropean heart journal. Digital health2025
Evaluating large language models in echocardiography reporting: opportunities and challenges.
Article in European heart journal. Digital health, 2025. 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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation.Journal of cardiovascular imaging · 2026Review
- Special article-EchoPeer: a standardized framework for assessing echocardiography reports in the era of artificial intelligence: a recommendation from the Korean Society of Echocardiography AI and Future Strategy Committee.Journal of cardiovascular imaging · 2026Review
- Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.Journal of medical Internet research · 2026Article
- Artificial intelligence in breaking the learning curve for echocardiography: a secondary analysis of a multicentre trial.European heart journal. Digital health · 2026Article
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- EchoAtlas: A Conversational, Multi-View Vision-Language Foundation Model for Echocardiography Interpretation and Clinical Reasoning.medRxiv : the preprint server for health sciences · 2026Article
- EchoGraph system for automated quality assessment of echocardiography reports.NPJ digital medicine · 2025Article
- The evaluation illusion of large language models in medicine.NPJ digital medicine · 2025Article
- Large Language Model Assistant for Emergency Department Discharge Documentation.JAMA network open · 2025Article
- A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Decoding large language models for radiology: strategies for fine-tuning and prompt engineering.Radiology advances · 2025Review
- Generative AI models: the next anaesthetic agent?British journal of anaesthesia · 2025Article
- Fine-tuned large language models can generate expert-level echocardiography reports.European heart journal. Digital health · 2025Article
- Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era.Journal of cardiovascular echographyReview
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Authors and funding
14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Aims: The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results: Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from 'Findings' to 'Impressions.' Against cardiologist-generated Impressions, the models' performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness ( Conclusion: EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.
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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.