  Recently, significant efforts have been made to create Health Digital Twins
(HDTs), digital twins for clinical applications. Heart modeling is one of the
fastest-growing fields, which favors the effective application of HDTs. The
clinical application of HDTs will be increasingly widespread in the future of
healthcare services and has a huge potential to form part of the mainstream in
medicine. However, it requires the development of both models and algorithms
for the analysis of medical data, and advances in Artificial Intelligence (AI)
based algorithms have already revolutionized image segmentation processes.
Precise segmentation of lesions may contribute to an efficient diagnostics
process and a more effective selection of targeted therapy. In this paper, a
brief overview of recent achievements in HDT technologies in the field of
cardiology, including interventional cardiology was conducted. HDTs were
studied taking into account the application of Extended Reality (XR) and AI, as
well as data security, technical risks, and ethics-related issues. Special
emphasis was put on automatic segmentation issues. It appears that improvements
in data processing will focus on automatic segmentation of medical imaging in
addition to three-dimensional (3D) pictures to reconstruct the anatomy of the
heart and torso that can be displayed in XR-based devices. This will contribute
to the development of effective heart diagnostics. The combination of AI, XR,
and an HDT-based solution will help to avoid technical errors and serve as a
universal methodology in the development of personalized cardiology.
Additionally, we describe potential applications, limitations, and further
research directions.
