This week in Cardiovascular AI
Summarizing the recent literature on Cardiovascular AI
Welcome to to This Week in Cardiovascular AI - a bi-monthly email newsletter for followers and subscribers that summarizes recent research in Cardiovascular AI and Digital Health that we find important and unique. For each edition we will summarize timely publications that are important in this space. This newsletter is a companion to CardioCyper, a long-form blog dedicated to the expanding intersection of Digital Health, Artificial Intelligence, Machine Learning and Healthcare, especially Cardiology and Electrophysiology.
A lot of good research in this edition. We start with an excellent review of how AI is transforming cardiovascular population health. Happy reading!
Artificial Intelligence to Improve Cardiovascular Population Health: Key Findings
A recent European Heart Journal review by Meder et al. (2025) provides a comprehensive analysis of AI’s transformative role in cardiovascular population health, addressing its applications in prevention, diagnosis, treatment, and public health strategies. The review explores real-world implementations of AI, highlighting both its potential benefits and the challenges associated with large-scale adoption.
1. AI in Cardiovascular Disease Prediction and Prevention
AI is enhancing risk stratification and disease prediction by integrating multi-modal data (e.g., electronic health records, wearable device data, imaging, and genetics).
Predictive AI models: AI algorithms are now outperforming traditional risk calculators in predicting cardiovascular events by identifying subtle, non-linear associations across vast datasets.
Population-scale risk prediction: AI-driven public health initiatives are being used to predict cardiovascular disease (CVD) risk based on social determinants of health, allowing targeted interventions for high-risk groups.
2. AI-Enhanced Wearables and Remote Monitoring
The review emphasizes the role of AI-powered wearable devices and remote monitoring platforms in improving early detection and long-term disease management.
AI-enabled ECG screening: Machine learning algorithms applied to single-lead and 12-lead ECGs can detect atrial fibrillation, ischemic heart disease, and heart failure with higher accuracy than traditional ECG interpretation.
AI in continuous monitoring: Smartwatches and portable ECG monitors are helping identify subclinical arrhythmias, which could lead to earlier stroke prevention strategies.
3. AI for Cardiovascular Drug Development
AI is playing a major role in accelerating drug discovery and personalized treatment strategies.
Target identification: AI-driven molecular modeling tools are identifying novel drug targets for heart failure and lipid management, improving precision therapy development.
AI-powered clinical trials: Machine learning is optimizing patient recruitment, trial design, and real-world validation, reducing the cost and time required for new cardiovascular therapeutics.
Precision medicine: AI is helping predict individual responses to antihypertensive and lipid-lowering drugs, leading to more personalized treatment approaches.
4. AI in Cardiovascular Imaging and Diagnostics
AI-driven imaging analysis has improved diagnostic accuracy and reduced the burden on clinicians by automating complex assessments in cardiac CT, MRI, and echocardiography.
Automated echocardiographic analysis: AI is improving left ventricular function assessment and diastolic dysfunction detection, offering real-time decision support for cardiologists.
AI-enhanced cardiac CT and MRI: Machine learning models are improving plaque characterization and ischemia detection, leading to more precise intervention planning.
5. AI’s Role in Addressing Modifiable Risk Factors
AI-based interventions are shaping lifestyle modification programs for hypertension, obesity, diabetes, and smoking cessation.
AI-driven weight management and exercise programs: AI-powered apps and wearables provide personalized recommendations based on an individual’s activity levels, metabolic profile, and cardiovascular risk.
Smoking cessation and alcohol reduction: AI chatbots and natural language processing (NLP) models are being used to detect risky behaviors and offer real-time coaching for behavior change.
6. AI and the Impact of Climate Change on Cardiovascular Health
The review highlights AI’s role in environmental health, particularly in understanding the effects of air pollution, urbanization, and climate change on cardiovascular disease.
AI-driven exposome modeling: AI algorithms are being applied to analyze the relationship between pollution, heat waves, and cardiovascular mortality, helping design public health interventions.
Urban health initiatives: AI-powered environmental monitoring systems are tracking air quality, noise pollution, and extreme weather events, supporting real-time public health recommendations.
7. Regulatory Challenges and Ethical Considerations
Despite its promise, AI adoption in cardiovascular healthcare faces major legal and ethical challenges:
Data privacy concerns: AI models rely on vast amounts of patient data, raising concerns about patient consent, algorithmic transparency, and bias.
Regulatory frameworks: The European Union AI Act and FDA guidelines are shaping AI’s role in medicine, ensuring that AI-powered diagnostic tools meet stringent safety and efficacy standards.
Healthcare equity: AI models trained on biased datasets may inadvertently widen health disparities, necessitating rigorous validation across diverse populations.
Conclusion
The review concludes that AI has the potential to revolutionize cardiovascular healthcare, offering early disease detection, precise diagnostics, personalized treatment, and improved population health management. However, careful regulatory oversight, ethical considerations, and equitable access must be addressed to ensure AI benefits all patients while minimizing systemic biases and unintended consequences.
🔗 Read the full study: European Heart Journal
Machine Learning vs. Deep Learning for Ablation Site Classification in Cloud-Based AI Systems
A new Heart Rhythm study by Narita et al. (2025) compares the performance of machine learning (ML) and deep learning (DL) algorithms for ablation site classification in a cloud-based AI system (CARTONET). With pulmonary vein isolation (PVI) remaining the cornerstone of atrial fibrillation (AF) ablation, automated systems for ablation site classification and reconnection prediction could improve procedural accuracy and efficiency. This study evaluates CARTONET’s transition from an ML-based model (R12.1) to a deep learning-based model (R14) in a cohort of 396 AF patients.
Key Findings
The study assessed 39,169 annotated ablation points and 625 predicted reconnection segments using CARTONET’s automated anatomical classification model. The deep learning-based R14 model demonstrated superior performance compared to the ML-based R12.1 model:
Sensitivity improved from 71.2% (R12.1) to 77.5% (R14) (p<0.0001), indicating better accuracy in identifying ablation sites.
Positive predictive value (PPV) improved from 85.6% (R12.1) to 86.2% (R14) (p=0.0184), reflecting more precise classification of ablation points.
In patients who underwent only PVI (PVI group), sensitivity increased from 75.3% to 81.3%, while for those who underwent additional ablations (PVI+ group), sensitivity increased from 58.2% to 64.9%.
The highest rate of reconnection was observed in the posterior left pulmonary vein (49.1%), followed by the posterior right pulmonary vein (41.2%).
The study also examined CARTONET’s ability to predict potential reconnection sites. While the reconnection prediction accuracy remained relatively low (PPV ~10%), the R14 model retained the same anatomical trends as R12.1, suggesting further refinement in reconnection site learning methodologies is needed.
Clinical Implications
The findings consider deep learning as a superior tool for ablation site classification, enhancing automation in catheter ablation procedures. AI-driven cloud-based systems like CARTONET could reduce manual annotation time, improve procedural reproducibility, and enhance operator decision-making. However, further improvements in AI-driven reconnection prediction could optimize follow-up strategies and procedural planning.
🔗 Read the full study: Heart Rhythm
AI and Digital Tools Transforming Cardiovascular Clinical Trials
A new European Heart Journal state-of-the-art review by Hu et al. (2025) examines how artificial intelligence (AI) and digital health technologies are reshaping the design and execution of cardiovascular clinical trials. While AI offers faster data processing, enhanced case selection, and real-time patient monitoring, several barriers—including regulatory concerns, data integration challenges, and disparities in technology access—must be addressed to fully realize AI’s potential in trial optimization.
AI-driven tools have significantly improved case screening and data adjudication by automating disease classification and identifying eligible trial participants. For example, deep learning (DL) models have successfully identified left ventricular dysfunction and aortic stenosis from routine echocardiograms, reducing the burden on clinicians and improving trial efficiency. AI has also been used to mine electronic health records (EHRs) for undiagnosed cardiovascular conditions, leading to better patient recruitment for clinical trials. Additionally, AI-guided natural language processing (NLP) has been applied to rapidly screen medical records for trial eligibility, significantly reducing the time required for manual chart reviews.
Wearable and remote monitoring technologies are becoming integral to cardiovascular trials. AI-enabled wearable ECGs, photoplethysmography sensors, and smartwatches allow for continuous patient monitoring, improving trial data collection and reducing the need for in-person follow-ups. In trials requiring serial imaging, AI-assisted portable echocardiography has demonstrated feasibility in remote settings, enabling more inclusive and decentralized trial designs. AI-powered adaptive enrichment strategies have also been implemented, adjusting participant recruitment dynamically based on predicted response to treatments, leading to smaller sample sizes and more efficient trial execution.
Despite these advances, significant roadblocks remain. The lack of standardized regulatory frameworks for AI-driven tools hinders their widespread adoption in clinical trials. Additionally, interoperability issues between digital platforms, limited digital literacy among trialists, and privacy concerns regarding AI-driven data processing pose challenges. The review underscores the need for clear regulatory guidelines, standardized reporting practices, and equitable access to digital tools to ensure AI-driven trials benefit all patient populations.
This review highlights that AI and digital technologies have the potential to revolutionize cardiovascular clinical trials, making them more efficient, cost-effective, and inclusive. However, successful implementation will require collaborative efforts between regulatory agencies, trialists, and technology developers to address technological, ethical, and logistical barriers in the adoption of AI-driven solutions.
🔗 Read the full study: European Heart Journal
AI-Enhanced ECG Predicts Mortality in Congenital Heart Disease: A Large-Scale Study
A new European Heart Journal study by Mayourian et al. (2025) introduces a deep-learning model using artificial intelligence-enhanced electrocardiography (AI-ECG) to predict 5-year mortality in pediatric and adult patients with congenital heart disease (CHD). CHD affects millions globally, requiring lifelong cardiac surveillance. Traditional risk assessment models rely on costly imaging and specialist expertise, but this study suggests that AI-driven ECG interpretation could provide an inexpensive and scalable alternative for risk stratification.
The study was conducted using data from Boston Children’s Hospital, analyzing over 225,000 ECGs from nearly 80,000 patients, with a median age at ECG of 7.7 years (range: 0–92 years). The AI-ECG model was trained on half the cohort and tested on the remaining half, followed by temporal validation on a contemporary cohort. The primary outcome was 5-year mortality, with 4.9% of training and 4.6% of test cohort patients experiencing mortality within that timeframe.
Key Findings
The AI-ECG model significantly outperformed conventional mortality predictors such as age, QRS duration, QTc interval, and left ventricular ejection fraction (LVEF).
The model achieved an AUROC of 0.79 (95% CI: 0.77–0.81) for 5-year mortality prediction, exceeding the performance of LVEF (AUROC 0.62), QRS duration (0.57), and QTc duration (0.48).
AI-ECG successfully risk-stratified CHD subgroups, with particularly strong predictive value in conditions like coarctation of the aorta (AUROC 0.86), tetralogy of Fallot (ToF), hypoplastic left heart syndrome (HLHS), and cardiomyopathies.
Kaplan–Meier survival analysis demonstrated that patients classified as high risk by AI-ECG had significantly lower long-term survival, with 15-year survival rates of 80% vs. 96% for high-risk vs. low-risk groups (HR 4.9, p < 0.001).
Saliency mapping and waveform analysis identified specific high-risk ECG features, including QRS fragmentation in ToF and low-amplitude, wide QRS complexes as markers of increased mortality risk.
Clinical Implications
This study establishes AI-ECG as a promising tool for CHD risk stratification, enabling early intervention, personalized follow-up plans, and improved resource allocation. AI-ECG could serve as a surveillance tool for routine cardiology visits, helping to prioritize imaging and interventions for high-risk patients while potentially reducing unnecessary testing for low-risk individuals. In low-resource settings, AI-ECG could enhance cardiac monitoring and improve access to CHD care, particularly in regions where advanced imaging and subspecialists are scarce.
While these findings highlight AI’s transformative potential in CHD management, future work will need to focus on multicenter validation, prospective trials, and integration with existing clinical workflows to ensure safe and equitable deployment.
🔗 Read the full study: European Heart Journal
Visualizing Bias in AI-Generated Medical Images: Implications for Healthcare
A recent European Heart Journal article by Szymański et al. (2025) explores the biases embedded in artificial intelligence (AI)-generated medical images and their impact on public perception and healthcare decision-making. As AI-generated imagery becomes increasingly integrated into medical education, patient communication, and healthcare marketing, its potential to reinforce stereotypes and misrepresent health conditions raises concerns about fairness, accuracy, and ethical AI use.
The study highlights how AI-generated images influence how diseases, patients, and healthcare professionals are visually represented. When tasked with generating images using prompts such as “cardiac patient” and “oncological patient”, popular AI image-generation tools (DALL-E, StarryAI, and OpenArt AI) produced highly stereotyped depictions. Cardiac patients were overwhelmingly portrayed as older White males, while oncological patients were more diverse in terms of gender, age, and race. Additionally, the cardiac patient images tended to be more abstract and symbolic, whereas oncological patient images were highly detailed and personalized. These findings suggest that AI-generated medical images may inadvertently perpetuate outdated stereotypes, which could skew public perception of disease risk and influence clinical decision-making.
Further investigation into gender bias in AI-generated professional imagery revealed that male professionals were depicted in 76% of AI-generated images, compared to only 8% for female professionals, across fields including medicine, law, and engineering. When researchers attempted to challenge AI-generated stereotypes—by prompting images of Black African doctors treating White children—the AI models failed to generate accurate representations, often misidentifying patient and doctor identities or reinforcing existing biases.
Addressing Bias in AI-Generated Healthcare Images
The study underscores the need for greater awareness of AI biases in healthcare and proposes strategies to mitigate them. These include:
Standardized guidelines for AI-generated medical images, ensuring diversity and realism in disease representation.
Bias auditing of AI tools used in medical education and marketing to prevent reinforcement of stereotypes.
Improved training datasets that represent a more balanced, demographically diverse population.
Regulatory oversight to ensure that AI-generated healthcare content does not mislead patients or clinicians.
As AI continues to shape healthcare communication and digital diagnostics, addressing these biases is critical to ensuring equitable healthcare representation and preventing misinformation.
🔗 Read the full article: European Heart Journal
AI-Based ECG Analysis for Hypertrophic Cardiomyopathy: Real-World Clinical Validation
A recent JACC: Clinical Electrophysiology study by Desai et al. (2025) evaluates the real-world performance of AI-driven electrocardiographic (AI-ECG) analysis for detecting hypertrophic cardiomyopathy (HCM). Given the high prevalence of undiagnosed HCM (up to 85%), this study examines whether AI-ECG can enhance early detection and risk stratification in routine clinical settings.
Study Design and Patient Cohort
The study prospectively analyzed 103,492 ECGs from 45,873 patients at the Cleveland Clinic between February 19, 2024, and November 1, 2024. The AI model, Viz-HCM (developed using convolutional neural networks), was deployed for real-time screening of HCM.
Patients flagged by the AI model were classified into three groups:
Group A: Patients with a known prior diagnosis of HCM (40.4%).
Group B: Patients without prior HCM diagnosis who were newly identified by AI (5%).
Group C: Patients flagged by AI but ultimately diagnosed with alternative conditions such as aortic stenosis, hypertensive heart disease, or ischemic cardiomyopathy (54.6%).
Key Findings
AI-ECG demonstrated high diagnostic accuracy, but performance varied depending on the chosen probability threshold:
At a threshold of ≥0.85, sensitivity was 100%, specificity was 98.4%, and the positive predictive value (PPV) was 45.3%.
At a threshold of ≥0.90, sensitivity dropped to 80.1%, but PPV improved to 54.9%.
At a threshold of ≥0.95, sensitivity decreased further to 49.8%, but PPV was the highest at 65.7%.
The AI model performed similarly in men and women and was more sensitive in patients <50 years old, whereas it was more specific in those ≥50 years. The model also performed well in patients with and without prior myectomy and showed consistent accuracy in those on mavacamten therapy, a targeted HCM drug.
Clinical Implications
This study underscores the clinical utility of AI-ECG as a cost-effective, scalable tool for HCM screening. By identifying 5% of patients with previously undiagnosed HCM, AI-ECG could enable earlier interventions and targeted genetic screening of at-risk family members. The study also highlights the importance of selecting an appropriate probability threshold—higher thresholds improve specificity and reduce false positives, while lower thresholds enhance sensitivity for early detection.
Future Directions
While AI-ECG shows promise as a screening tool for HCM, further research is needed to refine its use in low-prevalence populations and improve false-positive rates. Additionally, the study calls for the development of workflow strategies to ensure flagged patients receive appropriate follow-up imaging and genetic testing.
🔗 Read the full study: JACC: Clinical Electrophysiology
AI-Driven ECG Interpretation Achieves Expert-Level Pediatric Diagnosis
A recent JACC: Clinical Electrophysiology study by Mayourian et al. (2025) evaluates an AI-powered deep neural network for automated diagnosis of pediatric electrocardiograms (ECGs). Given the shortage of pediatric cardiology expertise worldwide, this study investigates whether AI-enhanced ECG interpretation can match or surpass expert-level accuracy in detecting cardiac abnormalities in children.
Study Design and Patient Cohort
The study analyzed 583,134 ECGs from 201,620 pediatric patients (median age: 11.7 years) at Boston Children’s Hospital, spanning 2000 to 2022. The AI model was trained on 437,350 ECGs and tested on a separate 145,784 ECG dataset. Each ECG was originally interpreted by expert pediatric cardiologists with >5,000 ECG reads, ensuring high-quality labeled training data. The model's primary objective was to classify ECGs as normal or abnormal, with secondary outcomes assessing its performance in detecting Wolff-Parkinson-White syndrome (WPW) and prolonged QTc.
Key Findings
The AI-ECG model demonstrated high diagnostic accuracy, surpassing conventional commercial ECG interpretation software (MUSE, GE Healthcare):
Any ECG abnormality: AUROC 0.94
WPW detection: AUROC 0.99
Prolonged QTc detection: AUROC 0.96
During readjudication of discordant ECGs, four blinded expert electrophysiologists were more likely to agree with the AI model than with the original ECG interpretation for:
Any abnormality detection (p = 0.001)
WPW (p = 0.01)
Prolonged QTc (p = 0.07)
In a time-to-diagnosis analysis, patients who had false-positive AI classifications were twice as likely to have a confirmed abnormal ECG on follow-up (HR: 2.0, p < 0.001), suggesting that the model identified subtle preclinical abnormalities.
Subgroup Analysis and Model Generalizability
Performance was consistent across different age groups, with a notable dip in accuracy for neonates (<1 week old), likely due to rapid ECG changes in early life. The model performed equally well for males and females, and its accuracy remained stable across all pediatric age groups and congenital heart disease subtypes.
Clinical Implications
This AI-ECG model offers expert-level pediatric ECG diagnosis, with potential applications in:
Expanding access to pediatric cardiology expertise, particularly in low-resource settings.
Supporting large-scale pediatric ECG screening programs, including sudden cardiac arrest risk assessment in young athletes.
Reducing cardiologist workload by automatically classifying normal ECGs, allowing specialists to focus on complex cases.
Future Directions
The study highlights the need for multicenter validation, real-world clinical trials, and the development of AI-ECG models that use image-based ECG inputs to improve accessibility in low-resource settings. Future efforts should focus on optimizing AI thresholds for different clinical use cases and integrating AI-ECG models into routine pediatric screening programs.
🔗 Read the full study: JACC: Clinical Electrophysiology