_applied sciences_  
_Article_  
Will Artificial Intelligence Provide Answers to Current Gaps  
and Needs in Chronic Heart Failure?  
[PERSON] 1, [PERSON] 1,*\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\), [PERSON] 3\({}^{\copyright}\) and [PERSON] 4,*\({}^{\copyright}\)  
\({}^{1}\) Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy; [EMAIL_ADDRESS]  
Footnote 1: email: [EMAIL_ADDRESS]  
Footnote 2: email: [EMAIL_ADDRESS]  
Footnote 3: email: [EMAIL_ADDRESS]  
I, including patients with no symptoms, nor limitation of physical activity, to NYHA class IV where patients are unable to carry on any physical activity without discomfort and symptoms at rest can be present [8].  
The burden of chronic heart failure is continuously growing, and an integrative approach is needed to counteract its impact. On the one hand, CHF is projected to increase progressively: HF currently affects approximately 6 million Americans, with its prevalence projected to increase by 46 per cent by 2030 [9]. The reasons are multiple, including population ageing, increased life expectancy, growing cardiovascular risk burden in developing countries, and improvement in cardiovascular treatments allowing more patients to grow old and eventually develop CHF. On the other hand, advancing diagnostic and therapeutic options brings a progressive increase in healthcare and societal costs, overshooting the current capacity of healthcare systems and public budgets worldwide. Hospitalisations are the most significant component of direct medical expenses that will reach 53 billion dollars in the US [10].  
In this context, artificial intelligence (AI) [11] is a promising tool that might positively impact multiple hurdles related to the ever-complicated management of heart failure. With its remarkable ability to analyse large volumes of physiological data obtained from thousands of patients, it can assist clinical practice and decision-making in a much more accurate and selective way than the human brain [12]. AI will be essential in analyzing raw image data from cardiac imaging techniques (such as echocardiography, computed tomography, and cardiac MRI) and electrocardiogram recordings through an algorithm. Multiple AI algorithms are currently able to automatically segment vessels or cardiac structures and to extract useful information, including standard clinical measures and additional indices such as the vessel fractal dimension (FD), capturing the complexity of collateral circulation networks [13; 14; 15]. Its adoption in the future will more closely approximate human decision-making, potentially augmenting cardiologists' real-time performance in emergency rooms, catheterisation laboratories, imaging suites, and clinics [16].  
Yet, the limits and potentials of AI have yet to be discovered entirely. This article provides an overview of current gaps and needs in CHF research and clinical management, reviewing currently available and under-development AI-based tools that might answer those gaps and need [17] as reported in Figure 1.  
The articles and tools mentioned in our work are the results of non-systematic research on currently published literature regarding the use of AI technologies for the management of HF patients. The search was carried out on PubMed, Google Scholar, and Scopus using the keywords: \"chronic heart failure\", \"artificial intelligence\", \"machine learning\", and \"deep learning\". Well-known studies are also included. All types of articles analyzing the use of artificial intelligence in the management of patients with CHF are discussed.  
Figure 1: Relationship between artificial intelligence (AI) and cardiology, in particular in the management of heart failure: tools currently available and current gaps. Abbreviations: ECG, electrocardiogram, MRI, magnetic resonance imaging.