## 6 Current Research Focus  
Current healthcare research is strongly focused on the concept of personalisation and early prevention. In this context, AI can be a powerful tool for predictive modelling, e.g., through deep learning. A key challenge for the near future will be the development of lean, efficient, and highly integrated algorithms to improve the management of the patient with CHF, particularly to predict the risk of developing heart failure or its progression [68]. The added value of AI tools in this setting resides in (i) the efficiency of execution, (ii) the ability to measure and recognise additional pieces of information that are currently underestimated and/or under-recognised by human examiners, and (iii) their reproducibility. Wearable devices and remote monitoring will change the management of these patients, allowing a higher degree of personalisation, taking into account multiple individual characteristics such as genetics, lifestyle, and health history. Some of the ongoing projects are the AI-Powered Heart Failure Management System (AIHFMS) [16], and The Heart Failure Prediction with Deep Learning project [69], which aim at developing a deep learning algorithm to predict the risk of heart failure progression based on electronic health record data and wearable device data and based on imaging and lab results respectively [22]. Another example is the Cardihab project, a digital health platform developed by researchers at the University of Sydney that uses AI to personalise heart failure management. The platform tracks patients' symptoms, medication adherence, and lifestyle habits and uses AI algorithms to provide personalised feedback and treatment recommendations. [[https://cardihab.com/](https://cardihab.com/)]([https://cardihab.com/](https://cardihab.com/)) accessed on 5 April 2023. In the meantime, the Personalized Heart Failure Management with AI project will develop a personalised treatment plan for heart failure patients based on individual factors such as genetics, lifestyle, and health history. Another promising project is the continuous monitoring of patient devices in critical scenarios, such as for example the coaxial intraventricular pump, supporting the circulation in patients with severe failure of cardiac function. As an example of how this technology might be used in future clinical practice, Abiomed has already trained an AI algorithm to predict the next five minutes of a patient's arterial pressure based only on the prior five minutes of console data and has also developed AI algorithms to predict other parameters, such as stroke volume, left ventricular pressure and cardiac output. ([[https://www.abiomed.com/about-us/news-and-media/press-releases/fda-approves-data-streaming-impella-console-setting-stage-artificial-intelligence](https://www.abiomed.com/about-us/news-and-media/press-releases/fda-approves-data-streaming-impella-console-setting-stage-artificial-intelligence)]([https://www.abiomed.com/about-us/news-and-media/press-releases/fda-approves-data-streaming-impella-console-setting-stage-artificial-intelligence](https://www.abiomed.com/about-us/news-and-media/press-releases/fda-approves-data-streaming-impella-console-setting-stage-artificial-intelligence)) accessed on 5 April 2023, ref. [70] Among the major randomised trials, the TIM-HF2 trial evaluated the usefulness of a multicomponent system comprising a three-channel ECG, in HF patients with NYHA class II-III and a left ventricular ejection fraction (LVEF) under 45 per cent (PhysioMem PM 1000, GETEMED Medizin und Informationstechnik AG, Germany), a BP device, a weight scale, and an oxygen saturation device. The intervention, compared with usual care, was associated with an improvement of the clinical condition, measured as a lower number of days lost from unplanned HF-related hospital admissions and had lower all-cause mortality (HR 0.70, 95 per cent CI 0.50-0.96) [71].