Wearable electrocardiogram sensors have emerged on the market but lack the technology to interpret the results with high performance. This paper explores the potential for innovation in developing a heart-based language and applying natural language processing to detect and classify dangerous arrhythmia. In the AI Cardiologist project, we propose real-time heart monitoring and alerting dangerous arrhythmia using AI-based technology using RoBERTa transformers in large language models. The focus is on analyzing the domains of medical applications and introducing new business models to exploit the technology’s results. We confirmed that foundation models for ECG records created by deep learning methods could greatly enhance precision and effectiveness of heart health monitoring and detection of atrial fibrillation, enabling better healthcare and well-being. No similar solution is available on the market outside of hospital environments, giving the project results a high potential for commercialization.