The role of artificial intelligence in the diagnosis, risk stratification, and treatment of heart failure: A narrative review

Heart failure (HF) is a complex, multifactorial, and difficult-to-treat syndrome. Over the past years, a concerning increase in its global prevalence, mortality, costs, and burden on the healthcare system has been observed. The recent development of machine learning (ML), especially unsupervised and deep learning (DL) algorithms, offers a potential way to facilitate diagnosis, enable more precise treatment, and reduce both mortality and costs of HF patients. Especially unsupervised ML and DL present new opportunities for increased efficiency in clinical practice in cardiology. Unsupervised ML, e.g., enables novel phenogrouping of HF patients into high-risk groups and disease outcome prediction. Deep learning algorithms can enhance echocardiographic analysis by improving image quality and ECG interpretation, and by providing assistance and guidance to inexperienced cardiologists. However, substantial challenges related to generalizability, external validation, overfitting, model explainability, and ethical considerations currently severely limit the implementation of AI-based tools in real-world clinical practice. This review critically evaluates current AI models in HF, focusing on their roles in diagnosis, risk stratification, and treatment personalization, as well as the major challenges that restrict their application in clinical practice.