Key result
Large language models and machine learning show significant potential to improve precision and efficiency in atrial fibrillation care, despite challenges like algorithmic bias and hallucinations.
Why the study?
Atrial fibrillation is a prevalent and complex cardiac arrhythmia requiring multifaceted management strategies, prompting an exploration of the integration of LLMs and machine learning into AF care.
Generative AI and LLMs offer promising applications in atrial fibrillation care, including decision support and automated documentation, though significant ethical and technical challenges remain.
Atrial fibrillation (AF) is a prevalent and complex cardiac arrhythmia requiring multifaceted management strategies. This review explores the integration of large language models (LLMs) and machine learning into AF care, with a focus on clinical utility, privacy preservation, and ethical deployment. Federated and transfer learning methods have enabled high-performance predictive modeling across distributed datasets without compromising data security. LLMs enhance decision-making by synthesizing structured and unstructured data within electronic health records, supporting anticoagulation decisions, risk stratification, and treatment optimization. Additionally, these tools reduce clinician burden through automated documentation and improve patient engagement via personalized communication, chatbots, and remote monitoring platforms. Despite promising outcomes, challenges such as algorithmic bias, hallucinations, outdated knowledge, and limited explainability persist. Regulatory frameworks remain underdeveloped for continuously learning models, necessitating stronger oversight. Future directions emphasize the creation of cardiology-specific LLMs, multimodal data integration, and inclusive co-development with stakeholders. Overall, artificial intelligence-enabled tools show significant potential to improve precision, efficiency, and equity in AF care, provided their deployment remains ethically grounded and clinically validated.
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Tran et al. (2025) conducted a review in Atrial fibrillation. Large language models and machine learning was evaluated. Large language models and machine learning show significant potential to improve precision and efficiency in atrial fibrillation care, despite challenges like algorithmic bias and hallucinations.
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