ArticleEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2023
Accuracy and comprehensibility of chat-based artificial intelligence for patient information on atrial fibrillation and cardiac implantable electronic devices.
Article in Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- Effect of targeted education of patients with atrial fibrillation on unplanned cardiovascular outcomes: results of the multicentre randomized AF-EduCare trial.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Trial
- AI chatbots for health information seeking among Chinese patients with precancerous ENT lesions: a descriptive qualitative study.BMJ open · 2026Article
- Comparative analysis of Chinese large language model performance on atrial fibrillation questions.BMC cardiovascular disorders · 2026Article
- AI in Patient Care: Evaluating Large Language Model Performance Against Evidence-Based Guidelines for Pulmonary Embolism.Thoracic research and practice · 2026Article
- Accuracy and Reliability of AI Models in Emergency Myocardial Infarction Education.Emergency medicine international · 2026Article
- Feasibility and accuracy of wearable electrocardiogram monitoring in adult congenital heart disease.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- EHRA perspective on the digital data revolution in arrhythmia management: insights from the association's annual summit.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Review
- Reliability of large language models for reviewing research with artificial intelligence in cardiac electrophysiology using the European Heart Rhythm Association artificial intelligence checklist.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Article
- Generative artificial intelligence in cardiovascular specialty care: a scoping review.BMC nursing · 2025Article
- Article
- Applications of large language models in cardiovascular disease: a systematic review.European heart journal. Digital health · 2025Review
- The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.Herzschrittmachertherapie & Elektrophysiologie · 2025Review
- Artificial intelligence-assisted chatbot: impact on breastfeeding outcomes and maternal anxiety.BMC pregnancy and childbirth · 2025Article
- Evaluating performance of large language models for atrial fibrillation management using different prompting strategies and languages.Scientific reports · 2025Article
- AI-Driven Information for Relatives of Patients with Malignant Middle Cerebral Artery Infarction: A Preliminary Validation Study Using GPT-4o.Brain sciences · 2025Article
- PULSE survey: Population Survey on Knowledge, Gaps and Perception of Heart Rhythm disorders-an initiative of the Scientific Initiatives Committee of the European Heart Rhythm Association.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Evaluating large language models as patient education tools for inflammatory bowel disease: A comparative study.World journal of gastroenterology · 2025Article
- Assessing large language models as assistive tools in medical consultations for Kawasaki disease.Frontiers in artificial intelligence · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsNatural language processing chatbots (NLPC) can be used to gather information for medical content. However, these tools contain a potential risk of misinformation. This study aims to evaluate different aspects of responses given by different NLPCs on questions about atrial fibrillation (AF) and clinical implantable electronic devices (CIED). METHODS AND
resultsQuestions were entered into three different NLPC interfaces. Responses were evaluated with regard to appropriateness, comprehensibility, appearance of confabulation, absence of relevant content, and recommendations given for clinically relevant decisions. Moreover, readability was assessed by calculating word count and Flesch Reading Ease score. 52, 60, and 84% of responses on AF and 16, 72, and 88% on CIEDs were evaluated to be appropriate for all responses given by Google Bard, (GB) Bing Chat (BC) and ChatGPT Plus (CGP), respectively. Assessment of comprehensibility showed that 96, 88, and 92% of responses on AF and 92 and 88%, and 100% on CIEDs were comprehensible for all responses created by GB, BC, and CGP, respectively. Readability varied between different NLPCs. Relevant aspects were missing in 52% (GB), 60% (BC), and 24% (CGP) for AF, and in 92% (GB), 88% (BC), and 52% (CGP) for CIEDs.
conclusionResponses generated by an NLPC are mostly easy to understand with varying readability between the different NLPCs. The appropriateness of responses is limited and varies between different NLPCs. Important aspects are often missed to be mentioned. Thus, chatbots should be used with caution to gather medical information about cardiac arrhythmias and devices.
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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.