## 8 Future Perspectives  
Artificial intelligence will dramatically improve the usefulness of new and upcoming \"smart\" technologies, such as wearable devices, mobile health, and a number of different sensors. Leveraging on a large amount of reliable and precise data AI algorithm will process these data together with other data obtained through traditional methods (anamnesis, clinical reports, medical examination, subject's history) to obtain a more precise and reliable diagnosis and prognostic prediction, allowing a truly tailored approach to therapy. Multiple applications can be envisioned, ranging from health risk assessment for the development of heart failure, continuous monitoring of the health status and state of progression of CHF, smart interactive planning of clinical visits and examinations, early recognition of \"red flags\" announcing acute complications, prediction of the likelihood of responding to a particular treatment, and many more. Foremost, the new avenues opened up by the new possibilities brought about by AI solutions will need a change in our paradigm of clinical research, shifting from research models focused on testing a single experimental intervention to the utilisation and evaluation of protocols and approaches to a specific health issue. This would put the patients at the centre of focus and will allow us to evaluate truly personalised interventions. Integrating multiple data sources will allow a holistic view, data standardisation and real-time monitoring of heart failure patients, enabling early detection of changes in health status and triggering early intervention. Finally, AI tools will be very helpful to support the discovery and development of new therapies for heart failure, improving our understanding of the underlying mechanisms of the disease, and integrating large amounts of data from various sources. AI-powered tools will drive a dramatic improvement of in silico tools in drug development research. **Author Contributions:** Conceptualisation, F.B., S.D.R. and P.H.G. ; methodology, F.B. and P.H.G. ; resources, P.H.G. ; writing--original draft preparation, F.B., S.D.R. and P.H.G. ; writing--review and editing, D.T. and P.V. ; visualisation, F.B., S.D.R., D.T., P.V. and P.H.G. ; supervision, S.D.R., D.T., P.V. and P.H.G. ; project administration, S.D.R. and P.H.G. ; funding acquisition, P.H.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by grants from the Italian Ministry of University and Research (PNRR-National Center for Gene Therapy and Drugs based on RNA Technology--CN00000041), and from the Italian Ministry of Health (PSC SALUTE 2014-2020 -POS4 \"Cal-Hub-Ria\"-T4-AN-09 and PNRR-MAD-2022-12376814). P.V. and P.H.G. were supported by a grant from the Italian Ministry of Enterprises and Made in Italy (MISE): PON-VQA. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing is not applicable to this article. In fact, no new data were created or analysed in this study. **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: