Fleur V. Y. Tjong

This 2026 update of the European Society of Cardiology (ESC) Core Curriculum for the Cardiologist reflects contemporary and emerging requirements for the practice of cardiology and the resulting training needs. The document has three main parts:Section 1 outlining the main training requirements and their presentation in entrustable professional activities (EPAs).Section 2 explaining the requirements for training centres.Section 3 outlining the general skills, knowledge, and attitudes required in cardiology (chapter 1) - and eight chapters outlining EPAs in the different areas of cardiology (chapter 2 to 9). This update has been written over 9 months in a iterative process, involving over 90 representatives as per the initial curriculum, similar to that used for the previous iteration of the core curriculum in 2020. Though much of the 2020 core curriculum remains relevant and unchanged, there was a clear need to amend this previous curriculum to reflect the expanding roles of some areas of cardiology such as data science, digital health, and artificial intelligence. These items have now been specifically included along with general updates to various clinical EPAs due to changes in practice or evidence base.

The rapid expansion of interventional electrophysiology (EP) and cardiac implantable electronic device (CIED) therapies has outpaced the capacity of traditional apprenticeship-based training to deliver standardized, equitable, and patient-safe skill acquisition across Europe. This scientific statement of European Heart Rhythm Association (EHRA) of the European Society of Cardiology (ESC) synthesizes the current landscape of digitally assisted learning for EP/CIED, provides a structured framework for implementation, and defines the responsibilities of scientific societies in establishing trustworthy, competency-based pathways. The document first reviews the technical principles and available digital modalities, emphasizing alignment of tool selection with learning objectives and staged progression. It then contrasts these capabilities with real-world uptake, highlighting fragmented access and strong reliance on industry-mediated training, alongside the emergence of neutral access models such as the EHRA Simulation Village. A central suggestion is the adoption of proficiency-based progression supported by protocolized evaluation: predefined and validated performance metrics, benchmarking against expert standards, deliberate practice with structured feedback, and summative assessment prior to patient procedures. Key implementation challenges include resource disparities and geographic inequities, limits in simulation fidelity for team-based and contextual decision-making, governance of industry relationships, and the need for protected educational time. Pragmatic solutions include tiered certification tracks, hybrid models combining digital and supervised clinical training, regional simulation hubs, minimum simulator specifications, ongoing curriculum updates, and coordinated stakeholder collaboration. Priority research needs are defined around clinical outcome validation, optimal curriculum design, technology development, and implementation science.

Aims: Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results: An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion: The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Aims: Patterns in physical behaviour may be associated with an increased risk of ventricular arrhythmia. We examined associations between temporal dynamics in physical behaviour measured through wearable technology and the risk of ventricular arrhythmia Methods and results: The multicentre, prospective SAFEHEART study recruited 303 patients with an implantable cardioverter-defibrillator (ICD) from May 2021 to September 2022. Continuous physical behaviour data were collected over 365 consecutive days and nights using wearable accelerometers. We characterized each 28-day behavioural time series using statistical summary indices and deep representations learned by neural networks. Logistic regression analyses were used to estimate the associations between physical behaviour and the subsequent risk of ventricular arrhythmia as detected by ICD and treated appropriately. Predictive performance was assessed using k-fold cross-validation and quantified by the area under the receiver operating characteristic curve (AUROC). From the SAFEHEART cohort, 277 patients and 56 ventricular arrhythmia were analysed, contributing to a total of 64 995 days of behavioural data. Reduced numbers of daily physical activity bouts and low day-to-day variation in sleep characteristics were associated with an increased risk of an arrhythmic event. Deep representations improved the predictive performance, with an AUROC of 0.74 ± 0.05, compared with statistical summary indices with an AUROC 0.67 ± 0.14 (P = 0.05 for comparison). Conclusion: Our findings suggest that behavioural patterns captured through wearable technology are independently associated with ventricular arrhythmia, particularly when these are characterised by deep representations.