Precision cardiac electrophysiology: toward digital twins and beyond

Precision medicine is a healthcare approach that aims at tailoring prevention, diagnostics, and treatments to each patient’s unique genetic, molecular, physiological, and environmental profiles. While the fundamental idea of personalized medical care dates back thousands of years, only in recent decades has this approach gained momentum (1), driving a paradigm shift toward individualized models of physiology, risk, and therapeutic response. This shift was primarily driven by a growing recognition of the limitations of the “average patient” paradigm, a problem well known across scientific disciplines and historically illustrated in striking ways. As previously described (2), a classic example comes not from medicine, but from aviation. In 1952, U.S. Air Force Lt. Daniels analyzed the body measurements of over 4,000 pilots to design a cockpit that fit the “average” flyer. Surprisingly, he found that virtually no individual pilot matched the average ranges even across a few measured dimensions (3). This insight led to a revolutionary redesign of cockpits away from a one-size-fits-all model and toward adjustable, individualized controls, which greatly improved performance. Similar lessons have emerged in medicine and biology, where population-based generalizations fail to capture the underlying variability of individual entities (2).

The recent advancement of precision medicine has been enabled by remarkable progress in biomedical research, data analytics, and computational approaches. High-throughput “omics” technologies (such as genomics, transcriptomics, proteomics, and metabolomics) have made possible to capture molecular diversity in health and disease at an unprecedented scale. At the same time, the rapid decline in the cost of genome sequencing has brought individual genetic data into both research and clinical settings. Innovations in bioinformatics, machine learning, and artificial intelligence (AI) have also unlocked the potential of large and complex datasets, enabling deeper insight into patient-specific mechanisms and therapeutic responses. Moreover, wearable sensors, electronic health records, and real-time monitoring technologies now generate continuous streams of personal health data, further fueling individualized care. Increasingly, these diverse data sources are being integrated into computational frameworks that simulate physiological processes, offering predictive insight into disease progression and treatment efficacy. This integration of data-rich measurements and mechanistic in silico modeling has led to powerful new approaches in precision medicine, most prominently exemplified by the emerging paradigm of the digital twin. Interestingly, the development of this concept is also linked to the field of aviation. In fact, NASA pioneered early forms of digital twin technology the 1960s by using simulated replicas of spacecrafts to troubleshoot problems in real time and support critical decisions from Earth (4), as most notably happened during the Apollo 13 mission. While digital twin methods gained traction in manufacturing and engineering in the early 2000s, it was again NASA’s renewed work in the 2010s that brought formal definition and widespread attention to this approach, helping catalyze its adoption across diverse scientific domains, including healthcare (4, 5). As recently defined by the U.S. National Academies of Sciences, Engineering, and Medicine (6), a digital twin is “a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system of systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value.” Central to this definition is the bidirectional interaction between the physical and virtual systems, enabling dynamic updates and actionable insight (6).

Precision medicine in cardiac electrophysiology

The recent progress in precision medicine for cardiovascular applications has been closely intertwined with the growing adoption of digital twin approaches (7, 8). This can be clearly demonstrated by the case of cardiac electrophysiology (9, 10), which has become one of the most thoroughly characterized areas of physiology since the 1960s, given the major role of heart rhythm disorders in global morbidity and mortality. A solid mechanistic understanding of cardiac function has enabled the development of biophysically detailed computational models at multiple scales, from ion channel kinetics and cellular action potentials to tissue conductivity and whole-heart electrical activation (11). These models have been instrumental in advancing our understanding of both normal cardiac electrophysiology and disease mechanisms in pathologies such as heart failure (12) and atrial fibrillation (AF) (13). They have played a key role in refining cardiac pharmacology by elucidating drug mechanisms and optimizing therapeutic strategies, as well as in screening for potential cardiotoxic effects of new compounds (14). Many frameworks have also incorporated electromechanical coupling, enabling simulation of contractile behavior and its modulation under physiological and pathological conditions (15). As these computational efforts have matured, growing attention has turned to a critical limitation of traditional modeling approaches: their reliance on mean values of key parameters averaged across cells, animal models, or patient populations. Although such average-based models have yielded valuable insights, they often obscure individual-specific features that can profoundly influence cardiac function (2). Small differences in ion channel function or repolarization reserve can alter the electrical behavior of the cardiomyocyte, tipping the balance toward arrhythmogenesis in some individuals but not in others. Similarly, in pharmacological studies, this averaging can mask therapeutic failure or proarrhythmic effects that only become apparent in specific patient subgroups. In response to this “flaw of averages”, novel paradigms have begun to explicitly incorporate biological variability through the simulation of populations of in silico models (2). These approaches not only enable a more robust characterization of physiopathology mechanisms through systematic sensitivity analyses that identify key drivers of pathological phenotypes (16), but also improve the assessment of drug responses by allowing in silico clinical trials across diverse virtual populations (17). Notably, novel optimization techniques have also been proposed to reparametrize average-based models based on sample- or patient-specific data, laying the groundwork for the development of personalized frameworks (2).

These advances have paved the way for the implementation of digital twin technologies. By integrating patient-specific multimodal data (e.g. imaging, hemodynamics, and electrophysiological measurements), these multiscale models are used to support clinical decision making (5, 8). Digital twins are now being used to guide the mapping and ablation planning of complex arrhythmias such as ventricular tachycardia and AF, by identifying patient-specific arrhythmogenic substrates and optimal ablation targets (18). In AF patients, these models have been leveraged to predict stroke risk based on factors like fibrosis distribution, AF burden, and blood flow dynamics, enabling more personalized anticoagulation strategies (10). For patients at risk of sudden cardiac death, digital twins can simulate ventricular arrhythmia vulnerability, supporting individualized risk stratification beyond the traditional metrics like ejection fraction (8). In patients undergoing cardiac resynchronization therapy, digital twin simulations can be used to evaluate alternative pacing strategies and optimize lead placement (10).

While these examples underscore the transformative potential of digital twins for the management of heart rhythm disorders, several precision medicine applications in cardiac electrophysiology are independent of mechanistic simulations. In general, these approaches harness patient-specific data, often enhanced by machine learning and AI, to guide diagnosis and therapy. One prominent example is genotype-guided therapy for inherited arrhythmia syndromes. In conditions like long QT syndrome, Brugada syndrome, and catecholaminergic polymorphic ventricular tachycardia, genetic testing informs diagnosis, risk stratification, therapeutic choice, and family screening strategies (19, 20). In parallel, pharmacogenomics and biomarker-guided care are increasingly used to tailor antiarrhythmic therapy. Genetic variants affecting drug metabolism can influence individual responses to medications like flecainide or amiodarone (21), while blood-based biomarkers associated to inflammation, fibrosis, and autoimmune mechanisms offer predictive value for arrhythmias risk (22). Analysis of microRNAs from blood samples is also emerging as a promising strategy for early arrhythmic risk assessment (23). Lastly, AI-driven interpretation of electrocardiogram and wearable data has become a powerful tool for early detection of AF and quantification of ectopic burden (24), enabling personalized clinical alerts and improving timely diagnosis.

Future directions and priorities

To maximize the clinical impact of precision medicine in cardiac electrophysiology, future research must extend and improve these approaches, a focus well aligned with the mission of the Journal of Precision Medicine: Health and Disease, which is uniquely positioned to highlight and disseminate advancements in these critical areas (25).

First, advancing fundamental mechanistic studies is needed to further deepen our understanding of the key subcellular processes underlying cardiac pathophysiology. This includes expanding knowledge in areas that remain understudied, such as the role of sex differences (26, 27), the impact of aging (28), and the influence of comorbidities on heart function. The novel insights will not only be essential for driving progress across the entire field, but also for informing the next generation of mechanistic models. Improving the translational relevance of data from preclinical models, including animal studies and patient-derived induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) is also critical (29). One important modeling challenge remains capturing the biological heterogeneity among patients (5), which necessitates innovative computational approaches. Lastly, strengthening multiscale integration, particularly by bridging molecular-level mechanisms with cellular and tissue levels (30), will be key to building more physiologically faithful and predictive computational frameworks.

As previously highlighted by Bhagirath and colleagues (10), current digital twin implementations face critical limitations that must be addressed to ensure the safe and effective clinical translation of these technologies. One major issue is that most existing digital twins are primarily anatomically personalized, incorporating imaging-derived geometries and structural information, but lack individualized calibration of parameters needed to reproduce patient-specific electrophysiology. Functional personalization remains a significant challenge due to the limited observability of many physiological variables in vivo. In fact, biophysical parameters can either be directly measured (which is rarely feasible in patients) or indirectly inferred through data assimilation techniques. Individual electrophysiological properties could be assessed with the analysis of patient-derived iPSC-CMs, though challenges remain related to their maturity and phenotypic variability (31). Another promising approach is genotype-informed modeling (32), which aims to capture known electrophysiological consequences of specific genetic variants. Looking ahead, emerging techniques such as single-cell genotyping and spatial transcriptomics offer new opportunities to resolve cellular heterogeneity and spatial organization within cardiac tissue (33), enabling further refinement of digital twin models. Another intrinsic challenge lies in the high dimensionality and complexity of multiscale models, which leads to substantial parameter uncertainty and complicates the process of model calibration (8, 10). Accurate personalization requires identifying a subset of parameters that can be uniquely and robustly inferred from available patient data, a nontrivial task given the often limited observability of physiological variables. Advanced optimization techniques, informed by global sensitivity analysis and integrating a priori physiological constraints, are needed to prioritize the most influential parameters and focus the calibration process. Model validation poses a related and equally critical challenge. Rigorous testing of digital twin predictions against empirical data is essential to build confidence in their clinical utility. However, validating patient-specific predictions is inherently difficult, as the outcomes required to test them may not yet exist in the real world. This lack of ground truth limits the ability to definitively assess model accuracy. To address both calibration and validation issues, the adoption of formal frameworks for verification, validation, and uncertainty quantification is thereby essential (34). A defining feature (and ongoing challenge) of digital twins is their capacity to evolve alongside the patient. To support adaptive therapy and dynamic risk assessment, these models must be periodically updated in response to clinical interventions, new comorbidities, or lifestyle changes. Achieving this goal requires improved pipelines for the seamless integration of diverse data sources, including structural imaging, electrophysiological and hemodynamic measurements, medical records, and wearable sensor data (10). Such integration is essential for generating anatomically and functionally accurate digital twins that can be reparametrized over time to reflect the patient’s evolving physiological state.

Advances in machine learning and AI are poised to transform nearly every facet of precision medicine in cardiac electrophysiology (35), including development of multiscale models and calibration/refinement of digital twins (36). These novel technologies also hold promise for accelerating complementary approaches (such as genotype-guided therapy, pharmacogenomics, and blood-based biomarker discovery) and for improving real-time risk stratification by integrating diverse data streams into more accurate and adaptive predictive tools. As AI-enabled systems mature, ensuring their transparency, interpretability, and seamless integration into clinical workflows will be critical for maximizing their impact on patient care (37). More broadly, the widespread adoption of digital twins and other precision medicine tools will require robust regulatory frameworks and standardized pipelines to support the incorporation of modeling outputs into electronic health records and clinical decision-support systems. With these advances come important ethical and regulatory considerations, particularly concerning safety, accountability, and patient data privacy (7). Equitable access must also be prioritized to prevent the exacerbation of existing disparities in healthcare (4, 5). Clinical trials have historically underrepresented women, racial and ethnic minorities, and underserved populations, limiting the generalizability of findings and reinforcing structural inequities (38). Rare diseases face additional barriers due to small cohort sizes and limited data availability. Overcoming these challenges will require regulatory oversight tailored to emerging technologies, rigorous implementation research, and a sustained commitment to equity, inclusion, and the responsible use of precision medicine to reduce (not widen) health disparities.