Artificial Intelligence and Digital Innovation in Cardiovascular Medicine

Abstract

Advances in digital innovation and data science are rapidly transforming cardiovascular medicine, with artificial intelligence, machine learning, and digital health tools now moving from experimental applications into clinical practice. This review provides a comprehensive overview of developments across the cardiovascular care continuum, with a particular focus on Asian populations where the burden of disease is high and health care systems face unique challenges. We highlight the role of large-scale cohort studies, risk prediction algorithms, multimodal artificial intelligence models, and digital innovations such as wearable devices and assistive clinical tools in prevention, diagnosis, treatment, and follow-up. Furthermore, we discuss barriers to implementation, including data quality, infrastructure, and regulatory frameworks. By integrating perspectives from laboratory science to bedside practice, this review outlines both the current state of research and the translational pathway required to achieve precision cardiology that is equitable, scalable, and sustainable.

Central Illustration

Highlights

• •AI and digital innovation have begun to transform all aspects of the cardiovascular care continuum.
• •Opportunities, progress, and challenges are highlighted in this review with focus on the Asian perspective.
• •Issues such as equity, generalizability, validation, regulatory frameworks, and economic feasibility remain.

Artificial intelligence (AI) and digital innovation enables rapid, accurate interpretation of complex biomedical data, facilitating early disease detection, prognosis, and personalized treatment.

The Asia-Pacific (APAC) region is home to approximately 4.84 billion people, representing around 60% of the global population of 8.09 billion.¹ Cardiovascular disease (CVD) remains the leading cause of death in the region, accounting for an estimated 58% of global CVD mortality (approximately 18.6 million deaths). Notably, the rate of CVD mortality in Asia is the only one increasing worldwide, with a yearly change of 0.36 per 100,000 between 1990 and 2019.² Given the sheer size and growth of the population, there is an urgent need to reduce cardiovascular mortality through improvements in prevention, early diagnosis, and treatment.

The epidemiological patterns of CVD in Asian populations differ markedly from those observed globally. Certain arrhythmias are more prevalent (eg, Brugada electrocardiogram [ECG] pattern); ischemic heart disease tends to present at an earlier age; and genetic, dietary, and environmental factors contribute to distinct risk profiles. These variations underscore the importance of population-specific approaches to cardiovascular care.

Despite the high burden of disease, the region faces multiple challenges. Health system disparities, particularly between urban and rural areas, limit access to advanced care. Genetic diversity across ethnic groups introduces unique risk factors that are often under-represented in global guidelines. In addition, data availability remains fragmented, making it difficult to implement large-scale, evidence-based interventions.²

Background

The concept of AI as harboring potential to augment or even replicate human cognitive capabilities dates back to mid-20th century milestones. Early pioneers such as Alan Turing explored the idea of “machine intelligence,” laying the theoretical foundation for what would later become AI in medicine. Over subsequent decades, progress was hampered by limited computational power, lack of large structured data sets, and rudimentary algorithms, which constrained early medical AI to simple rule-based expert systems and basic decision-support tools.³

From the 1980s and through the 1990s, interest in AI in health care grew slowly but steadily. Expert systems for diagnostic support, early computer-aided detection, and decision support were explored, though many systems were brittle, domain-specific, and unable to generalize across patient populations or settings.⁴ The 2000s saw the emergence of more powerful machine learning techniques, particularly deep learning architectures, which enabled the processing of large volumes of data (eg, imaging, electronic health records [EHRs]) and more complex pattern recognition tasks.⁵ Natural language processing also matured, allowing extraction of information from textual medical records, clinical notes, and literature, with contribution to richer models of patient state and risk.

Key milestones in the evolution of AI in health care include regulatory and commercial acceptance. For example, deep learning–based algorithms for imaging diagnostics achieved human-level accuracy or better in certain tasks; tools became approved by regulatory bodies for clinical use; and AI integration into telemedicine and patient monitoring increased, especially during emergency contexts such as the COVID-19 pandemic when remote care and digital tools became essential.⁶ In addition, AI has been leveraged not only for diagnosis, but for treatment planning, operational logistics (eg, scheduling, workflow), personalized medicine (using genomic and “omics” data), and predictive analytics for patient outcomes and hospital resource use.

Despite the promise, adoption has been uneven. Obstacles include technical limitations (bias, lack of interpretability, data quality, and heterogeneity), organizational and user concerns (clinician trust, gaps in training, fears about role displacement), ethical and legal issues (data privacy, liability, consent), and regulatory/regimen challenges (approval pathways, reimbursement, standards).⁷

Recent years have seen an acceleration in momentum. The number of publications and experimental deployments has grown significantly, showing that AI research in medicine is shifting from proof-of-concept to real-world implementation studies.⁴ Also, efforts toward explainability, transparency, fairness, and governance are being more explicitly addressed in study designs and policy discussions.⁷ These developments illustrate that integration of AI into health care systems must still overcome substantial multidimensional challenges for widespread, safe, and equitable impact.

Prevention

CVD prevention has historically rested on broad population-level measures and individual risk factor modification that are grounded in epidemiologic evidence accumulated over decades. Classical approaches prioritize lifestyle interventions, such as dietary modification, smoking cessation, regular physical activity, and weight management, alongside the control of biomedical risk factors including hypertension, diabetes, and hyperlipidemia. The success of these strategies is evident in the substantial decline in CVD mortality observed across many high-income countries since the 1970s, a trend largely attributed to reductions in tobacco use and improved management of blood pressure and cholesterol.⁸ However, traditional approaches are often constrained by reliance on population averages, limited individualization, and difficulties in sustaining long-term behavioral changes. Prevention efforts remain highly unequal across populations with persistent disparities in access, adherence, and health literacy, particularly in low- and middle-income countries (LMICs) and marginalized communities.⁹

The conventional prevention paradigm has relied heavily on standardized risk calculators (eg, Framingham Risk Score), which estimate future cardiovascular risk based on clinical variables (eg, age, sex, smoking status, blood pressure, and cholesterol levels).¹⁰ Whereas these tools provide clinically useful benchmarks, their performance is suboptimal across diverse ethnic and geographic populations, often underestimating or overestimating risk in non-Western cohorts.¹¹ Furthermore, they tend to apply static thresholds that fail to capture the dynamic and multifactorial nature of cardiovascular risk over time. These limitations underscore the need for more sophisticated models that can integrate broader determinants of health, adapt dynamically, and provide tailored recommendations at both the population and individual levels.

AI and digital health technologies are increasingly being explored as tools to address these gaps. By leveraging data from EHRs, wearable sensors, imaging, and genomic data sets, AI enables a more precise, personalized, and predictive approach to prevention. At the population level, machine learning algorithms can identify high-risk communities by analyzing geospatial, socioeconomic, and environmental data, such as diet, air pollution, and health care utilization.^12,13 Such predictive approaches are particularly relevant in LMICs and the APAC region, where surveillance infrastructures are often limited and prevention strategies are underfunded.

At the individual level, AI-driven models have demonstrated superiority over conventional risk scores by capturing complex, nonlinear relationships among diverse risk factors. For example, machine learning models trained on EHR and imaging data have outperformed pooled cohort equations in predicting incident atherosclerotic cardiovascular disease and have achieved higher discrimination and calibration.¹⁴ The integration of polygenic risk scores into these frameworks further enhances predictive accuracy, particularly for individuals with a strong genetic predisposition to CVD,¹⁵ but genetic analyses, in particular those that use microarray analytical methods, are tailored to genetic backgrounds (eg, European, East Asian). Importantly, these approaches have the potential to extend precision prevention to previously underserved populations (eg, APAC, LMICs) if data sets are adequately representative.

A further dimension of AI in prevention lies in the integration of novel biomarkers. Conventional markers such as low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and glycated hemoglobin remain central to risk stratification, but recent advances in proteomics, metabolomics, and transcriptomics have expanded the biomarker landscape. Machine learning applied to these high-dimensional data sets has identified new biomarkers predictive of early subclinical CVD and heart failure, including inflammatory mediators and lipid subfractions.¹⁶ Incorporating such markers into AI-driven models may allow earlier identification of at-risk individuals, enabling targeted interventions before clinical disease manifests, and enable a shift from reactive to proactive prevention.

Lifestyle modification remains the cornerstone of prevention, yet sustained behavioral change has long proven difficult. Traditional interventions, such as face-to-face counselling and structured cardiac rehabilitation programs, are effective but resource-intensive and often poorly adhered to outside of clinical trial settings.¹⁷ Digital health technologies, enhanced by AI, are increasingly being deployed to support adherence to diet, exercise, and medication regimens. Wearables and smartphones can continuously monitor physical activity, dietary intake, and sleep, then feed these data into algorithms that provide real-time personalized feedback and coaching.¹⁸ Randomized controlled trials suggest that such dynamic, personalized approaches are more effective than static advice alone, leading to improved adherence and sustained behavioral change.¹⁹

Nevertheless, the application of AI in prevention is not without challenges. Many predictive models have been developed using data sets derived from Western populations, limiting their generalizability to APAC or LMIC contexts.²⁰ There is also a risk of exacerbating health inequities if digital prevention tools, such as wearables or genomic testing, remain unaffordable or inaccessible to disadvantaged groups. The “digital divide” may lead to systematic exclusion of those most in need of preventive interventions, thereby widening disparities in cardiovascular health outcomes. Furthermore, many AI systems function as “black boxes,” providing predictions without transparent rationale, which might undermine clinician trust and patient acceptance. Regulatory frameworks for validating and approving AI-based preventive tools remain in evolution with few systems yet demonstrating long-term benefit in prospective, real-world trials.

In summary, the prevention of CVD has traditionally relied on population-based interventions and individual risk factor modification, which have achieved notable successes but remain constrained by limitations of generalizability, personalization, and sustainability. The integration of AI and digital technologies is expected to address these challenges by enabling more precise, predictive, and proactive prevention strategies. Through improved risk prediction, novel biomarker integration, and support for sustained behavioral change, AI has the potential to transform preventive cardiology into a dynamic, personalized discipline. However, significant barriers related to equity, generalizability, and regulatory oversight must be addressed to ensure these advances deliver population-wide benefits rather than exacerbating existing disparities (Figure 1).

Figure 1.

Use of AI in Prevention

Prevention of cardiovascular disease has traditionally relied on population-based interventions and individual risk factor modification which have achieved notable successes but remain constrained by limitations of generalizability, personalization, and sustainability. The integration of AI and digital technologies is expected to address these challenges by enabling more precise, predictive, and proactive prevention strategies.

Diagnosis

The diagnosis of CVD has been traditionally anchored in a structured framework that integrates clinical evaluation, electrocardiography, echocardiography, advanced imaging, and laboratory investigations. This systematic approach has facilitated advances in the identification and management of cardiac disorders, yet it remains constrained by limitations of sensitivity, specificity, operator dependence, and inequalities in access across populations. AI and digital technologies are rapidly reshaping the diagnostic landscape, offering opportunities to enhance accuracy, reproducibility, and timeliness.

Clinical assessment remains the foundation of CVD diagnosis.²¹ However, clinical interrogation and physical examination are subject to significant interobserver variability and dependence on clinician expertise, limiting their reliability as standalone diagnostic tools. The ECG and biomarkers (eg, troponin) also aid in diagnosis.²² More recently, portable consumer ECG devices have extended access to cardiac monitoring outside hospital settings, allowing earlier detection of atrial fibrillation and other arrhythmias.²³ However, interpretation remains vulnerable to human error, whereas widespread adoption of consumer-led ECG monitoring raises the risk of false positives and overdiagnosis.

The noninvasive, portable, and relatively low-cost nature of echocardiography has made it the first-line imaging modality in suspected heart failure, valvular heart disease, and structural abnormalities.²⁴ Nevertheless, echocardiography is heavily operator-dependent, with both acquisition and interpretation subject to significant variability. In resource-limited settings, a lack of trained echocardiographers poses major barriers to timely diagnosis, often delaying initiation of treatment.²⁵

Advanced imaging modalities such as computed tomography (CT),²⁶ cardiac magnetic resonance imaging²⁷ and nuclear cardiology techniques (eg, positron emission tomography and single-photon emission CT) provide further diagnostic precision, particularly in complex or equivocal cases. Despite their strengths, these modalities are expensive, often limited to tertiary centers, and remain inaccessible in many LMICs and underserved APAC regions, contributing to diagnostic inequity.²⁵

Laboratory investigations (eg, troponin, natriuretic peptides, lipid and glycemic profiles) are another cornerstone of cardiovascular diagnosis.^21,22 Genetic testing is increasingly being used in select cases, such as familial hypercholesterolemia, hypertrophic cardiomyopathy, or arrhythmogenic syndromes.²⁸ Yet routine integration of genomic and multiomic diagnostics remains limited by cost, uncertain utility, and lack of established frameworks for clinical implementation.

Limitations in diagnostic approaches persist as traditional approaches often rely on population-derived thresholds, such as defining heart failure with reduced ejection fraction as <40%, which may not capture disease heterogeneity or early dysfunction.²⁹ Many investigations, such as echocardiography, are subject to interobserver variability, thus limiting reproducibility. Advanced modalities, though powerful, remain geographically and financially inaccessible to much of the global population. Most significantly, conventional diagnostic pathways often identify disease only at advanced, symptomatic stages, and undermine the potential for prevention and early intervention.

AI offers a transformative solution to many of these challenges. In electrocardiography, deep learning algorithms have been shown to match or exceed cardiologist-level accuracy in detecting arrhythmias, left ventricular dysfunction, and electrolyte disturbances.³⁰ Importantly, such models can identify subclinical disease signatures in apparently normal ECGs, predicting future risk of disease. Automated interpretation using AI also reduces interobserver variability and increases diagnostic throughput.

Cardiac imaging generates vast quantities of complex data, making it ideally suited to AI-driven analysis. AI has been applied to cardiac CT and cardiac magnetic resonance imaging, enabling automated plaque characterization, perfusion quantification, and tissue mapping, often outperforming traditional semiautomated techniques.³¹ These tools are particularly valuable in resource-limited settings, where they can compensate for the shortage of specialist interpreters by providing automated, high-quality analysis.

Laboratory medicine has likewise benefitted from AI. Machine learning approaches have optimized rule-in and rule-out strategies using high-sensitivity troponin data, reducing false positives while maintaining safety.³²

Nonetheless, barriers remain to widespread adoption. Many AI diagnostic tools are trained on Western data sets, raising concerns about external validity and potential biases when applied to Asian or African populations.²⁰ Black-box algorithms challenge clinician trust and accountability, particularly in high-stakes scenarios such as suspected myocardial infarction. Integration into clinical workflows is another hurdle, because tools must be designed to complement, and not disrupt, established pathways. Finally, there is a risk of over-reliance on automated outputs, necessitating safeguards to ensure that clinical judgment remains central.

In conclusion, cardiovascular diagnostics have traditionally relied on structured clinical, imaging, and laboratory approaches that have enabled great progress but remain constrained by variability, inequity, and delayed detection. AI offers the potential opportunity to transform this paradigm by delivering more accurate, reproducible, and integrated diagnostic models. Yet realizing this potential will require addressing the challenges of generalizability, transparency, and implementation. If successfully deployed, AI-enabled diagnostics could mark a decisive shift toward precision, equity, and earlier intervention in cardiovascular care (Figure 2).

Figure 2.

Use of AI in Diagnosis

Diagnostics have traditionally relied on structured clinical, imaging, and laboratory approaches that have enabled great progress but remain constrained by variability, inequity, and delayed detection. AI offers the potential opportunity to transform this paradigm by delivering more accurate, reproducible, and integrated diagnostic models.

Treatment

The treatment of CVD has historically been anchored in evidence-based guidelines developed by societies such as the European Society of Cardiology, the American College of Cardiology/American Heart Association, and national bodies across the APAC region. These frameworks draw on large-scale randomized controlled trials to provide structured algorithms for conditions such as hypertension, acute coronary syndromes, myocardial infarction, heart failure, and valvular heart disease.^21,33 Population-derived thresholds allow clinicians to adopt standardized approaches to risk factor modification and disease management.³⁴ This population-based model has driven major advances in outcomes, particularly in high-income settings where access to pharmacologic and interventional therapies is widespread but its standardized approach underestimates the heterogeneity of treatment response, particularly across diverse ethnic and socioeconomic groups.³⁵

Pharmacologic therapy remains the foundation of CVD treatment (eg, hypertension,³⁴ dyslipidemia,³⁶ and heart failure²¹). These pharmacotherapies are supported by robust trial data demonstrating reduced morbidity and mortality; however, challenges remain in interindividual variation in efficacy and tolerability. Genetic polymorphisms also add to patient-tailored approaches (eg, SLCO1B1 mutations associated with statin-induced myopathy³⁷).

Interventional and surgical approaches are also cornerstones of treatment (eg, percutaneous coronary intervention,³⁸ structural intervention³⁹). In parallel, device-based therapies such as implantable cardioverter-defibrillators, pacemakers, and cardiac resynchronization therapy have significantly reduced mortality and improved quality of life in patients with arrhythmia and heart failure.⁴⁰ Despite these advances, interventional and device therapies are unequally distributed globally, with stark contrasts between high-income countries and LMICs. In much of the APAC region, limited infrastructure and financial barriers restrict patient access to advanced interventions.³⁵

A critical element of cardiovascular treatment is ongoing monitoring and therapy adjustment.^21,34 Despite established frameworks, monitoring remains predominantly reactive because treatment adjustments are typically made after deterioration rather than in anticipation. Delay in identification contributes to adverse outcomes (eg, heart failure decompensation, bleeding complications).

AI potentially enables a shift from reactive to predictive monitoring. Continuous data from wearables, implantable devices, and remote monitoring platforms are increasingly analyzed in real time using machine learning. In heart failure, remote monitoring systems coupled with AI algorithms have successfully predicted hospitalizations days before clinical deterioration, allowing for earlier therapeutic intervention.⁴¹ Similar systems are being explored for arrhythmia monitoring, anticoagulation safety, and detection of adverse drug reactions. Integration of these tools with EHRs further enables automated alerts and supports timely clinical decision-making while reducing clinician burden.

Despite these advances, the translation of AI into cardiovascular treatment faces significant challenges. A key concern is explainability because many models function as black boxes, raising questions about accountability and clinician trust. Integration with hospital information systems is often technically complex, requiring interoperability standards that are not universally in place. Costs and infrastructure requirements may widen disparities in access, particularly between high-income and resource-constrained settings. Finally, the value of clinical judgment and patient preference must be preserved to ensure that algorithmic recommendations augment rather than replace shared decision-making.

In summary, treatment of CVD has evolved significantly through evidence-based guidelines, pharmacologic innovations, and interventional breakthroughs. However, the continued reliance on population-derived thresholds and reactive monitoring limits personalization and predictive capacity. AI and digital technologies provide a potential opportunity to transform treatment into a proactive, individualized, and adaptive processes. By integrating multimodal data, predicting therapeutic responses, and enabling continuous monitoring, AI has the potential to bridge many of the gaps inherent in traditional care. Nevertheless, the success of this transition will depend on ensuring equity, transparency, and effective integration into clinical workflows worldwide (Figure 3).

Figure 3.

Use of AI in Treatment

Treatment has evolved significantly through evidence-based guidelines, pharmacological innovations, and interventional breakthroughs. However, the continued reliance on population-derived thresholds and reactive monitoring limits personalization and predictive capacity. AI and digital technologies provide a potential opportunity to transform treatment into a proactive, individualized, and adaptive processes.

Follow-Up

Traditional follow-up strategies have been guided by evidence-based frameworks developed by societies such as the European Society of Cardiology, American College of Cardiology/American Heart Association, and those in the APAC region. These recommendations typically define fixed intervals for reassessment, with additional visits prompted by symptomatic deterioration or hospital readmission. While structured approaches have demonstrably reduced morbidity and mortality, they remain episodic, reactive, and tailored to the dynamic trajectories of individual patients.

The frequency and timing of traditional follow-up visits vary according to disease state and therapy.^21,22,42, 43, 44 A structured framework is useful but fails to capture the highly variable progression of CVD, particularly in multimorbid or elderly patients.

Medication adherence and lifestyle compliance are additional focal points of monitoring.⁴⁵ However, adherence assessment continues to rely heavily on patient self-reporting, which is prone to recall and reporting biases. Remote consultations, increasingly adopted during and after the COVID-19 pandemic, have expanded access and convenience, but variability in engagement persists across socioeconomic and cultural contexts, particularly in low-resource environments.⁴⁶

Despite widespread adoption, traditional follow-up strategies suffer from several inherent limitations. Their reliance on predetermined intervals and static thresholds often results in delayed detection of deterioration, with interventions initiated only after the onset of clinically significant symptoms or abnormal results on investigations. Population-based protocols also fall short to adequately account for individual variability in disease trajectory with many patients either overmonitored or undermonitored relative to their true risk. Access and equity also remain critical challenges including geographic, financial, and workforce barriers that may limit consistent follow-up and contribute to disparities in outcomes.⁴⁷ These limitations underscore the need for more personalized, anticipatory, and integrated approaches, which are gaps that AI is increasingly positioned to address.

AI offers the potential to transform follow-up from a protocol-driven, episodic model into one that is continuous, predictive, and adaptive. Central to this shift is the widespread availability of wearable devices, implantable sensors, and home-based monitoring systems capable of generating continuous streams of physiological data. Metrics such as heart rate, rhythm, blood pressure, oxygen saturation, and weight can be tracked remotely and fed into machine learning algorithms capable of detecting early signs of instability. For example, deep learning applied to telemonitoring of heart failure patients has been shown to predict hospitalizations several days before they occur, enabling proactive interventions that reduce readmission risk.⁴¹ This marks a clear departure from the traditional reliance on scheduled, clinic-based assessments, and allows for a paradigm shift toward anticipatory care.

AI has also shown utility in predicting readmission risk and dynamically stratifying patients for follow-up. Hospital readmissions remain a persistent burden in cardiovascular care, particularly in heart failure and post–acute coronary syndromes patients. Machine learning models integrating EHRs, wearable data, and social determinants of health have been developed to identify patients at highest risk of readmission.⁴⁸ Unlike traditional models based on fixed clinical predictors, these algorithms can continuously update risk estimates as new data are incorporated, thus supporting adaptive scheduling of follow-up visits. This enables targeted allocation of resources to those most in need while reducing unnecessary follow-up in lower-risk individuals, which is a level of efficiency that is difficult to achieve with conventional methods.

AI can further enhance follow-up through improved interpretation of imaging and laboratory data. Traditional approaches rely on snapshots and binary cutoffs that risk overlooking gradual but clinically important trends. By contrast, machine learning models can analyze longitudinal data sets, identifying subtle changes across repeated echocardiograms, imaging scans, or biomarker measurements. For instance, algorithms trained on serial echocardiographic parameters have successfully predicted progressive declines in ventricular function before they reach guideline thresholds for intervention.⁴⁹

Despite these advances, AI-driven follow-up faces notable challenges. Access to digital technologies remains uneven, raising concerns of a digital divide that could exacerbate disparities in cardiovascular outcomes, particularly in LMIC and rural APAC populations. Data privacy and cybersecurity risks are also heightened by the continuous collection of sensitive health data, necessitating robust regulatory frameworks and technical safeguards.⁵⁰ From a clinical perspective, poorly designed systems risk overwhelming clinicians with unfiltered alerts, contributing to decision fatigue and reducing the effectiveness of monitoring programs. Furthermore, many AI applications in follow-up remain in the early stages of validation, with limited large-scale, multicenter trials demonstrating clear improvements in hard outcomes such as mortality and hospitalization. Regulatory approval, cost-effectiveness evaluations, and seamless integration into clinical workflows will therefore be essential before widespread adoption can be achieved.⁵¹

In summary, follow-up care for CVDs has traditionally been shaped by structured, guideline-based schedules and clinician-led reassessment, which have improved outcomes but remain constrained by their episodic, reactive, and generalized nature. The advent of AI offers the potential to reconceptualize follow-up as a continuous, personalized, and proactive process, harnessing remote monitoring, predictive analytics, and multimodal data integration to anticipate deterioration and optimize resource use. Realizing this vision, however, will require addressing challenges of equity, trust, data security, and validation to ensure that innovations in follow-up are accessible, reliable, and truly augment clinical decision-making (Figure 4).

Figure 4.

Use of AI in Follow-Up

Follow-up care has traditionally been shaped by structured, guideline-based schedules and clinician-led reassessment, which have improved outcomes but remain constrained by their episodic, reactive, and generalized nature. AI offers the potential to reconceptualize follow-up as a continuous, personalized, and proactive process, harnessing remote monitoring, predictive analytics, and multimodal data integration to anticipate deterioration and optimize resource use.

Research

Cardiovascular research has historically been centered on large-scale epidemiologic studies, multicenter registries, and randomized controlled trials, which have collectively laid the foundations for evidence-based practice. These methods, grounded in rigorous design and statistical inference, have enabled major advances in survival and morbidity reduction but they remain constrained by intrinsic limitations. Recruitment for large trials is expensive and time-consuming, often leading to selective participation skewed toward populations in high-income countries. Traditional statistical models, though robust, often assume linear associations and are limited in their ability to handle the complexity of multimodal data sets. Moreover, the growing burden of multimorbidity, particularly in aging societies, has highlighted the inadequacy of approaches that seek to isolate single-disease processes.

AI and digital technologies are increasingly positioned as tools to address these gaps, enabling cardiovascular research to shift from siloed, hypothesis-driven enquiry toward integrative, data-driven discovery. By leveraging advances in machine learning, natural language processing, and high-performance computing, researchers can interrogate EHRs, imaging repositories, wearable-derived data sets, and multiomic platforms at unprecedented scale. This capability is particularly relevant to CVD disease as a discipline characterized by heterogeneity in pathophysiology, variable trajectories of progression, and substantial interindividual differences in treatment response. Where traditional research provides population-level averages, AI offers the potential to uncover granular subtypes, novel biomarkers, and personalized therapeutic strategies.

One of the earliest applications of AI in research has been the automation of cohort identification and data harmonization. Clinical trial recruitment has historically relied on manual screening of records, a process prone to inefficiency and selection bias. Natural language processing applied to EHRs can rapidly extract relevant diagnostic codes, laboratory parameters, and unstructured clinical notes, thereby streamlining participant identification and improving representativeness of enrolled populations.²⁰

Risk prediction represents another area where AI is reshaping the research landscape. Conventional risk calculators such as Framingham or QRISK3 (QResearch Cardiovascular Risk Algorithm) are derived from regression models that incorporate a limited set of demographic and clinical variables. Although useful, they capture complex interactions among genetic, metabolic, lifestyle, and environmental factors. Machine learning models, by contrast, can integrate thousands of predictors simultaneously, identifying nonlinear patterns and interactions that traditional methods overlook. Studies have demonstrated that AI-enhanced risk scores improve discrimination for incident coronary heart disease, stroke, and heart failure compared with established risk calculators.¹⁴ These approaches not only enhance predictive accuracy but also generate new hypotheses for mechanistic exploration.

A particularly transformative contribution of AI to cardiovascular research lies in the integration of multiomic data sets. Advances in genomics, transcriptomics, proteomics, and metabolomics have generated unprecedented volumes of molecular data, yet traditional analytic approaches struggle to disentangle the complex, nonlinear relationships between these layers. Deep learning methods are capable of capturing high-dimensional interactions to enable the discovery of novel biomarkers, therapeutic targets, and gene–environment interactions. For example, metabolomic profiling analyzed through machine learning has revealed novel pathways of myocardial remodeling and fibrosis, thus providing potential targets for heart failure interventions.⁵¹ Such strategies hold particular promise in populations under-represented in genomic discovery efforts, including many APAC cohorts, where leveraging AI may accelerate the identification of population-specific risk alleles and therapeutic susceptibilities.

Wearables and mobile health platforms represent another frontier for cardiovascular research. Traditional epidemiologic studies have relied on periodic assessment conducted in clinical settings that provide only snapshots of physiological status. By contrast, wearable devices and smartphone applications enable continuous, longitudinal data collection and capture fluctuations in heart rate, activity, sleep, and rhythm at scale. These real-world data sets offer a richer understanding of behavioral and environmental influences on cardiovascular risk. Beyond individual risk prediction, such data can inform population-level research, particularly in geographically diverse or resource-limited settings where traditional surveillance infrastructure is lacking. Importantly, the decentralized nature of wearable-based research can reduce barriers to participation and enable inclusion of patients outside conventional academic medical centers.

Imaging research has similarly been transformed by AI. Traditionally, echocardiographic, CT, and magnetic resonance imaging data sets required manual interpretation, limiting scalability and introducing variability. Machine learning models can now automate image segmentation, quantify chamber volumes, detect subtle structural abnormalities, and extract radiomic features beyond the resolution of human observers.⁵² When applied across large imaging biobanks, these methods may facilitate discovery of novel imaging biomarkers predictive of cardiovascular outcomes. The ability to systematically mine imaging archives expands opportunities for retrospective research, reduces observer bias, and accelerates hypothesis generation.

Much of the reported performance of cardiovascular AI is derived from retrospective data sets, frequently from single-center tertiary cohorts. This leads to inherent selection and spectrum bias and optimistic estimates due to limited external validation. Small sample sizes, especially in subgroups such as sex, ethnicity, age, rurality, and comorbidity burden also increase instability of estimates. As a result, high area under the curve values in development cohorts may not translate to reliable clinical utility, particularly across heterogeneous settings. To improve interpretability and comparability across studies, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-style structured approach for evidence synthesis alongside AI-specific reporting guidance should be considered with studies aligned with SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials)-AI (for trial protocols) and CONSORT (Consolidated Standards of Reporting Trials)-AI (for AI intervention trials) guidelines.

In summary, whereas traditional research methodologies have provided the evidentiary backbone of cardiovascular medicine, their limitations in scale, diversity, and complexity necessitate complementary approaches. AI and digital technologies are not a replacement but an augmentation of tools that can expand discovery, improve precision, and democratize participation. The future of cardiovascular research will likely be defined by hybrid models that combine the rigor of randomized experimentation with the breadth of real-world, AI-enhanced data. Realizing this vision will require not only technological innovation but also careful attention to equity, governance, and interdisciplinary collaboration.

Discussion

The integration of AI and digital technologies into cardiovascular prevention, diagnosis, treatment, follow-up, and research represents a profound paradigm shift (see Central Illustration). The preceding sections have illustrated how traditional approaches that have evolved over decades of evidence-based medicine continue to provide robust and reliable frameworks for patient care. However, these methods remain inherently constrained by their reliance on episodic encounters, threshold-based decision-making, and clinician-dependent interpretation. AI and digital tools offer the potential to augment, and in some contexts transform, these foundations by enabling continuous monitoring, predictive modeling, and integrative data synthesis. The implications of this convergence highlight opportunities, challenges, and the balance required to integrate innovation without displacing the clinical wisdom embedded in established practice.

Central Illustration.

Artificial Intelligence Across the Cardiovascular Care Continuum

A feedback circle is formed including stages of prevention, diagnosis, treatment, follow-up and research.

A central theme emerging across domains is the transition from reactive to proactive care. Traditional cardiovascular practice, whether in prevention through guideline-directed risk stratification or in follow-up through scheduled consultations, often responds to disease once symptomatic or measurable deterioration has occurred. AI, by contrast, enables anticipatory strategies. This shift holds the promise not only of reducing morbidity and mortality but also of reshaping the economics of cardiovascular care, because earlier intervention generally reduces downstream costs. Nonetheless, this proactive capacity raises important questions about overdiagnosis and underdiagnosis, medicalization, and patient anxiety, particularly in contexts where predictive signals may not yet translate into actionable interventions.

Another recurring theme is the potential of AI to enable personalization in a field long reliant on population averages. Traditional approaches, though grounded in rigorous trial evidence, tend to apply recommendations uniformly and assume homogeneity across diverse patient populations, but heterogeneity is substantial in CVD. The trajectory toward precision requires careful consideration of equity and generalizability. Much of the current AI evidence derives from Western, tertiary-care cohorts, raising the risk of algorithmic bias and reduced applicability to APAC and LMIC populations where disease burden is disproportionately high.⁴⁷

The gap between technological potential and real-world implementation is also evident. Wearables, smartphone applications, and implantable sensors generate vast volumes of patient-generated health data, but translating these into actionable clinical insight remains complex. Issues of interoperability across health systems, validation of consumer-grade devices, and clinician workload need to be addressed. Moreover, the risk of alert fatigue is not trivial, because poorly triaged outputs can overwhelm clinicians and undermine confidence in AI systems. At the same time, for patients, digital technologies may democratize access by bringing monitoring into the home and reducing reliance on hospital infrastructure. However, this assumes equitable access to devices and digital literacy, which is not the case across socioeconomic strata, particularly in LMIC settings. The digital divide risks exacerbating disparities rather than alleviating them if not addressed through policy and infrastructure development.

In reflecting on clinical workflow, it is notable that AI is not designed to replace but rather to augment clinician expertise. Diagnostic imaging is a prime example of the potential of how AI-enhanced interpretation can identify subtle changes overlooked by the human eye,⁵² but final clinical decision-making remains rooted in the physician’s judgment. Similarly, predictive algorithms can guide follow-up scheduling or therapy escalation, but the ultimate responsibility lies with the clinician who must integrate these outputs with patient preferences, comorbidities, and social contexts. Physician involvement and governance remains essential for safety, ethical accountability, and trust. Importantly, clinicians must be equipped with the skills to critically appraise and effectively use AI tools. This demands substantial investment in education and training so that future practitioners are informed partners in human–machine collaboration.

From a research perspective, the impact of AI extends beyond clinical translation to the very design and conduct of studies. As highlighted, natural language processing and automated EHR curation can accelerate cohort identification, whereas federated learning may allow for multicenter collaborations without compromising data privacy.²⁰ Furthermore, the capacity of AI to integrate diverse data sets whether it be clinical, imaging, wearable, or multiomic, positions the technology as a driver of system-level discovery. However, the challenge lies in ensuring that the outputs of such research are interpretable and translatable. The black box nature of deep learning models remains a barrier to clinical adoption, with explainability emerging as a critical area of development. Without clarity on how predictions are generated, clinicians may be reluctant to act on them, and regulators may hesitate to approve them for widespread use.

The ethical, legal, and social implications of AI adoption merit attention. Data privacy is a paramount concern, particularly because continuous monitoring and cloud-based analytics generate sensitive personal health information. Regulatory frameworks must evolve to safeguard patient autonomy and consent while facilitating innovation. Liability is another unresolved issue.

Perhaps the most profound implication of this technological transformation lies in its potential to reshape the doctor–patient relationship. Traditional models center on episodic, face-to-face interactions, with clinicians acting as gatekeepers of medical knowledge. AI-enabled care disperses knowledge, decentralizes monitoring, and empowers patients to play a more active role in their health. Remote monitoring platforms and digital coaching applications exemplify this shift and foster partnership-based care. Though empowering, this shift also risks burdening patients with responsibility for their own data interpretation and decision-making. Striking a balance between empowerment and support is crucial to ensure that AI enhances, rather than undermines, the therapeutic alliance.

Taken together, the integration of AI into cardiovascular medicine is not merely a technical innovation but a sociotechnical transformation. Its success depends as much on governance, equity, education, and culture as on algorithmic accuracy. The traditional foundations of cardiology (eg, rigorous evidence, clinician expertise, and patient-centered care) remain indispensable. AI should be viewed not as a replacement but as a complementary layer, enabling continuous, personalized, and predictive care that traditional approaches alone cannot achieve. The challenge moving forward is to navigate this integration responsibly and ensure that innovation aligns with clinical realities and societal values.

Future Directions

The application of AI in cardiovascular medicine is still in its formative stages, yet the trajectory of progress suggests that the coming decade may be defined by a shift from experimental proof-of-concept studies to integrated, large-scale clinical deployment. For this transition to be realized, however, several critical challenges and opportunities must be addressed, ranging from the equity of research populations to the design of clinical trials and the development of regulatory frameworks.

One of the foremost priorities for the future lies in ensuring equity and generalizability. At present, the majority of cardiovascular AI models are trained on data derived from Western populations, and these models do not account for the diversity of genetic backgrounds, environmental exposures, and health care infrastructures that characterize the APAC region and LMICs.⁵³ This narrow representation risks producing algorithms that are less accurate, or even biased, when applied outside the contexts in which they were developed. To overcome these limitations, future research must actively expand recruitment to under-represented regions and leverage techniques such as federated learning, which allow models to be trained across multiple centers without requiring direct transfer of patient identifiable data.⁵⁴ Such strategies will be particularly important in balancing the need for diversity in training data sets with the imperatives of privacy and data security.

Beyond the composition of data sets, another pressing direction involves the integration of multimodal data streams. CVD is a complex, multifactorial condition, and outcomes are influenced by an interplay of clinical records, imaging findings, biomarker trends, lifestyle factors, and increasingly, genomic and other omic information. AI is uniquely positioned to synthesize these disparate inputs into predictive models that provide a more holistic understanding of patient trajectories. Early examples of multimodal neural networks have shown promise in identifying novel subgroups within conditions such as heart failure with preserved ejection fraction, yet broad implementation remains limited by challenges in data standardization and interoperability.⁵⁵ Future directions must therefore emphasize harmonized data infrastructures, shared clinical standards, and seamless integration into EHRs, with the long-term goal of establishing precision cardiology that can move beyond average treatment effects to tailored, dynamic interventions.

Achieving such integration will not, however, be sufficient unless clinicians can trust and understand the outputs of AI systems. A recurring criticism of current approaches is their reliance on black box algorithms, which may deliver high predictive performance but offer little transparency in how decisions are reached. In the context of high-stakes cardiovascular care, explainability is essential. The future of AI in cardiology must therefore prioritize explainable AI and incorporate interfaces that provide clinicians with interpretable insights, clear decision rationales, and the ability to challenge or override recommendations where necessary. Such co-designed systems, built with clinician input from the outset, will be fundamental to ensuring adoption at scale.

The transition from innovation to clinical impact will also depend heavily on rigorous validation. At present, most evidence for AI in cardiology comes from retrospective studies or small prospective cohorts, often within single centers. Large-scale, randomized controlled trials remain rare, and without them, it will be difficult to demonstrate superiority over current standards of care. Future priorities need to include multicenter, pragmatic trials that test not only whether AI tools improve outcomes, but also whether they can be feasibly integrated into real-world clinical workflows.⁵⁶ Alongside traditional trials, real-world evidence studies should be expanded to examine usability, cost-effectiveness, and patient acceptability. Such work will be critical to securing regulatory approval, reimbursement, and ultimately the widespread translation of AI into practice.

Finally, the sustainability of AI in cardiovascular medicine will hinge on the establishment of robust ethical, policy, and governance frameworks. As care becomes increasingly data-driven, issues of privacy, consent, and security will intensify. The sheer volume of information generated by continuous monitoring through wearables, implantable devices, and home-based platforms raises questions about ownership, stewardship, and the potential for misuse. Policymakers and regulators will need to create agile frameworks that protect patients without stifling innovation, while ensuring that the benefits of AI are distributed equitably rather than exacerbating existing disparities. This will be particularly important in APAC and LMIC settings, where digital infrastructure may lag behind and where there is a risk that advanced technologies remain accessible only to privileged subpopulations.

Beyond inequity and access, economic feasibility will likely be a primary determinant of whether AI-enabled cardiovascular care can scale in APAC and LMIC settings. Even where algorithms are accurate, adoption depends on whether health systems can justify up-front and recurrent costs against downstream savings. Robust health economic evaluation is therefore needed. Reimbursement remains a major unresolved barrier because many AI tools function as software-as-a-medical-device or workflow analytic tool for which payment pathways are often unclear.

Much of the current literature emphasizes coronary artery disease and heart failure, however important cardiovascular domains remain under-represented such as congenital heart disease (CHD), non-atrial fibrillation electrophysiology, pulmonary hypertension and vascular surgery. These under-covered areas represent high-yield priorities for APAC and LMIC settings where specialist capacity is limited and scalable decision-support could meaningfully improve access.

Taken together, the future of AI in cardiovascular medicine is likely to be characterized by a model of hybrid intelligence, in which the analytical power of algorithms is combined with the contextual knowledge and empathy of clinicians. If progress can be made in ensuring equitable datasets, developing multimodal precision tools, fostering explainability, conducting rigorous validation, and embedding robust governance, then AI has the potential not only to transform cardiovascular medicine but also to provide a template for the responsible integration of technology across healthcare more broadly. The challenge will lie not merely in developing novel models, but in ensuring that they are implemented in a way that enhances, rather than replaces, the clinician–patient relationship, and that they serve to narrow, rather than widen, global inequities in cardiovascular outcomes.

Conclusions

The evolution of cardiovascular medicine over recent decades has been defined by advances in prevention, diagnosis, and therapy, largely driven by population-level epidemiology and protocol-based care. These traditional approaches have yielded significant reductions in morbidity and mortality but remain constrained by episodic monitoring, reliance on clinician interpretation, and variable access to high-quality care. AI and digital technologies provide an opportunity to transcend these limitations by enabling continuous, data-driven, and individualized models of care, as earlier sections of this review demonstrated.

In prevention, AI-driven risk prediction tools and digital interventions support earlier identification of high-risk individuals and the adoption of sustained behavioral modifications and extend beyond the static nature of traditional risk scores. In diagnosis, machine learning has shown potential in ECG interpretation and cardiovascular imaging, and in reducing operator variability and expanding diagnostic reach. Reshaping of treatment strategies are likewise being considered, with predictive algorithms guiding therapeutic selection and procedural planning, while continuous monitoring enables timely adjustments. Follow-up care, traditionally reliant on periodic clinic visits, is being transformed by wearables and implantable devices that allow near real-time assessment of patient status and empower proactive management and reduce the likelihood of preventable hospitalizations. Research as well is undergoing a paradigm shift with AI-driven analysis of multimodal and multi-omic datasets refining phenotyping, uncovering novel biomarkers, and facilitating more inclusive, efficient trial design, with particular relevance to underrepresented populations in Asia.

However, there remain major challenges to clinical adoption. Algorithmic opacity, limited generalizability across diverse populations, concerns about data governance, and the risk of exacerbating health inequities underscore the need for careful implementation. As emphasized in prior sections, AI must be viewed not as a replacement for clinical expertise but as a complementary tool that augments decision-making, integrates seamlessly into workflows, and extends the capacity of health systems.

Taken together, the integration of AI into cardiovascular medicine offers the potential to redefine care as anticipatory, continuous, and patient-centered. The trajectory of the coming decade will determine whether these technologies, if developed responsibly and deployed equitably, can deliver on their promise to improve outcomes and transform cardiovascular health globally.

Funding Support and Author Disclosures

The Japan Heart Foundation, National Institute for Health Research (Leicester Biomedical Research Centre), the British Heart Foundation (BHF) including the Centre of Research Excellence Award (RE/24/130031), the Medical Research Council (MRC) UK Consortium on MetAbolic Phenotyping (MAP/UK), Grant-in-Aid for Scientific Research (A) (23H00454) and for Challenging Research (Pioneering) (22K18412) from the Japan Society for the Promotion of Science (JSPS), the LeDucq Foundation, and the Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” Grant Number JPJ012425 – all to TS. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.