Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis

Abstract

Heart failure (HF) is a chronic condition characterized by high morbidity and mortality worldwide, imposing a substantial burden on healthcare systems. In recent years, artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, have demonstrated great potential in HF management. By integrating multimodal data, such as electronic health records and medical imaging, AI models address limitations in risk prediction, phenotyping, diagnosis, treatment, and prognosis, offering novel insights to improve the quality of life for HF patients. However, several challenges remain before AI can be reliably implemented in clinical practice, including model selection, model generalization, interpretability, and limited reliability in real-world settings. In this review, we systematically summarize recent advances in application of AI in HF management across multiple domains, including inspection, monitoring, treatment, and integration. We further discuss key real-world challenges to implementation, and outline future directions for the development of intelligent HF management. In addition, representative application cases are presented to illustrate how AI technologies can be developed and translated into clinical practice, with the aim of providing practical insights and methodological guidance for researchers.