ArticleFrontiers in cardiovascular medicine2025
Assessing the performance of zero-shot visual question answering in multimodal large language models for 12-lead ECG image interpretation.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From Gram Stain to Decision Support: Performance of Multimodal Large Language Models in Blood Culture Microscopy.Journal of imaging informatics in medicine · 2026Article
- Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation.Journal of medical Internet research · 2026Article
- Artificial Intelligence for Biomedical Diagnostics: Diagnostic Accuracy and Reliability of Multimodal Large Language Models in Electrocardiogram Interpretation.Life (Basel, Switzerland) · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Benchmarking large language models on the United States medical licensing examination for clinical reasoning and medical licensing scenarios.Scientific reports · 2025Article
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Authors and funding
6 authors.
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Abstract
Large Language Models (LLM) are increasingly multimodal, and Zero-Shot Visual Question Answering (VQA) shows promise for image interpretation. If zero-shot VQA can be applied to a 12-lead electrocardiogram (ECG), a prevalent diagnostic tool in the medical field, the potential benefits to the field would be substantial. This study evaluated the diagnostic performance of zero-shot VQA with multimodal LLMs on 12-lead ECG images. The results revealed that multimodal LLM tended to make more errors in extracting and verbalizing image features than in describing preconditions and making logical inferences. Even when the answers were correct, erroneous descriptions of image features were common. These findings suggest a need for improved control over image hallucination and indicate that performance evaluation using the percentage of correct answers to multiple-choice questions may not be sufficient for performance assessment in VQA tasks.
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