Artificial intelligence–driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care–leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence–driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes.

The EU Horizon 2020 Framework-funded Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary (STOPSTORM) consortium has been established as a large research network for investigating STereotactic Arrhythmia Radioablation (STAR) for ventricular tachycardia (VT). The aim is to provide a pooled treatment database to evaluate patterns of practice and outcomes of STAR and finally to harmonize STAR within Europe. The consortium comprises 31 clinical and research institutions. The project is divided into nine work packages (WPs): (i) observational cohort; (ii) standardization and harmonization of target delineation; (iii) harmonized prospective cohort; (iv) quality assurance (QA); (v) analysis and evaluation; (vi, ix) ethics and regulations; and (vii, viii) project coordination and dissemination. To provide a review of current clinical STAR practice in Europe, a comprehensive questionnaire was performed at project start. The STOPSTORM Institutions’ experience in VT catheter ablation (83% ≥ 20 ann.) and stereotactic body radiotherapy (59% > 200 ann.) was adequate, and 84 STAR treatments were performed until project launch, while 8/22 centres already recruited VT patients in national clinical trials. The majority currently base their target definition on mapping during VT (96%) and/or pace mapping (75%), reduced voltage areas (63%), or late ventricular potentials (75%) during sinus rhythm. The majority currently apply a single-fraction dose of 25 Gy while planning techniques and dose prescription methods vary greatly. The current clinical STAR practice in the STOPSTORM consortium highlights potential areas of optimization and harmonization for substrate mapping, target delineation, motion management, dosimetry, and QA, which will be addressed in the various WPs.

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today’s computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field.

Regenerative medicine is searching for stem cells that can be safely and efficiently employed for regeneration of damaged solid organs (e.g., the heart, brain, or liver). Ideal for this purpose would be pluripotent stem cells, which, according to their definition, have broad potential to differentiate into all types of adult cells. For almost 20 years, there have been unsuccessful attempts to harness controversial embryonic stem cells (ESCs) isolated from embryos. Induced pluripotent stem cells (iPSCs), generated by genetic modification of adult somatic cells, are a more promising source. However, both iPSC and ESCs are associated with a risk of teratoma formation. At the same time, various types of more‑differentiated adult stem and progenitor cells derived from the bone marrow, umbilical cord blood, mobilized peripheral blood, or fat tissue are being employed in clinical trials to regenerate damaged solid organs. However, for most of these cells, there is a lack of convincing documentation for successful regeneration of the treated organs. Beneficial effects of those cells might be explained by paracrine effects of growth factors, cytokines, chemokines, bioactive lipids, and extracellular microvesicles, which are released from the cells and have trophic, antiapoptotic, and angiopoietic effects. Nevertheless, there is evidence that adult tissues harbor a promising population of very rare dormant stem cells with broad differentiation potential. In this review, we will discuss various potential sources of stem cells for regenerative medicine and the mechanisms that explain some of their beneficial effects as well as highlight the results of the first clinical trials.