The Heart Rhythm Society Just Published Its First-Ever Framework for AI in Electrophysiology. Here's What It Means.

On April 10, 2026, the Heart Rhythm Society's Digital Health Committee published a landmark scientific statement in Heart Rhythm laying out a comprehensive framework for how artificial intelligence and digital health technologies should be integrated into clinical electrophysiology workflows. This is a detailed, practical roadmap authored by twelve leading electrophysiologists and digital health experts from institutions including Massachusetts General Hospital, MIT, Stanford, Mayo Clinic, Emory, Johns Hopkins, and the VA system.

Here's a breakdown of everything that matters.

What the Statement Covers

The statement is organized around three pillars: (1) current and emerging AI applications in EP, (2) the decision-making process for adoption and implementation, and (3) how to evaluate safety and efficacy after deployment across the entire product lifecycle.

The AI Applications Already Transforming EP

The statement identifies three categories of AI/DHT application in electrophysiology: automation, organization, and prediction.

Automation is the most mature category. AI-driven ECG interpretation systems now use deep learning models trained on massive datasets and achieve diagnostic accuracy comparable to or exceeding expert cardiologists for arrhythmias including atrial fibrillation, ventricular tachycardia, and heart block. Cardiac implantable electronic devices (CIEDs) are evolving toward closed-loop systems where AI continuously adjusts pacing rates and sensing thresholds without clinician intervention. In ablation procedures, AI-enhanced mapping software is automatically identifying arrhythmogenic substrates like slow conduction zones and scar tissue, and randomized controlled trial evidence (the TAILORED-AF trial) now shows AI-guided ablation strategies improve arrhythmia-free survival in patients with persistent and long-standing persistent AF.

AI-driven remote monitoring triage is also highlighted. The statement acknowledges that continuous remote monitoring generates enormous data volumes, a "substantial proportion" of which is non-actionable. Advanced AI analytics that can separate truly actionable alerts from noise have the potential to significantly reduce clinical workload while increasing detection of clinically significant arrhythmias.

Organizational tools include ambient AI scribes that transcribe clinician-patient encounters into structured clinical notes, AI-powered EHR chatbots that draft responses to patient inquiries (medication refills, test result explanations), and third-party platforms that apply color-coded AI triage to device transmission data. The statement explicitly calls out that "prompt engineering", the ability to frame clear, context-rich queries for LLMs, should be treated as a necessary clinical skill and incorporated into EP training and continuing education.

Prediction is where AI offers "supra-human" capability. Key examples include predicting future atrial fibrillation from a 12-lead ECG, identifying prevalent cardiomyopathy, and predicting risk of sudden cardiac death, all from data sources where the standardization and sheer volume of ECG databases provide a critical opportunity. The statement notes that prediction using EHR data combined with imaging, telemetry, and genetic information is rapidly expanding.

The Adoption Decision: What Clinicians (and Vendors) Need to Know

This is where the statement gets most prescriptive. It lays out a five-point validation checklist that vendors should be able to address at minimum:

1. Underlying datasets: Device types, rhythm distributions, inpatient vs. outpatient recordings, labeling procedures, handling of poor-quality or ambiguous signals

2. Internal validation: Cross-validation or bootstrapping with discrimination estimates (sensitivity, specificity, PPV, NPV, AUC) across clinically relevant thresholds

3. External validation: Independent sites differing in geography, patient demographics, and hardware vendors, with transparent subgroup reporting by sex, race/ethnicity, age, comorbidities, and device type

4. Calibration and failure modes: False-alarm rates expressed in interpretable units (e.g., false alerts per 100 transmissions or per patient-month)

5. Prospective evaluation: Ideally starting with a silent phase, followed by randomized or pragmatic trials showing effects on process measures and patient outcomes

The statement is clear: despite sophisticated technology, "integration should be based largely on the well-established process of clinical trial evaluation." AI should be used as labeled by the FDA with respect to the patient cohort that has been evaluated. Off-label use introduces both clinical and legal risks.

Regulatory Landscape

The paper provides a detailed comparison of three regulatory regimes for adaptive AI/ML-based Software as a Medical Device (SaMD):

United States (FDA): AI devices are regulated under existing pathways, 510(k), De Novo classification, and premarket approval. The Pre-Cert pilot (launched 2017) formally concluded in 2022 and is not currently an active pathway. The FDA has published predetermined change control plans (PCCPs) that allow manufacturers to define the scope of future model changes at the time of marketing submission, enabling updates without repeated full submissions.

European Union (MDR + AI Act): The Medical Devices Regulation governs AI software, while the AI Act adds specific obligations for "high-risk" systems, which include most AI-enabled medical devices. The AI Act requires risk management, data governance, transparency, human oversight, and post-market monitoring.

United Kingdom (MHRA): The UK's SaMD/AiMD Change Programme targets software and AI across the entire lifecycle, aligning with GMLP (Good Machine Learning Practice) principles and international practice on adaptive AI.

All three jurisdictions have endorsed 10 Good Machine Learning Practice guiding principles emphasizing representative data, robust engineering, human-AI interaction, and lifecycle monitoring.

Reimbursement: Fragmented but Evolving

The statement is frank about reimbursement challenges. There is no single, stable reimbursement mechanism for EP-focused AI. Current coverage relies on several fragmented mechanisms:

• CPT codes 0764T and 0765T: Computerized ECG analysis with AI for detection of cardiac disease or risk-based assessment of cardiac dysfunction. CMS has begun establishing national payment rates for AI-enabled ECG tools (e.g., hypertrophic cardiomyopathy detection).

• CPT codes 0992T and 0993T: AI-enabled perivascular fat analysis from CT for noninvasive cardiac risk assessment.

• Category III codes for AI-enabled cardiac auscultation platforms combining digital stethoscopes with structural murmur, low ejection fraction, and arrhythmia detection algorithms.

• Remote therapeutic monitoring codes 98975-98981: Currently oriented toward respiratory, musculoskeletal, and cognitive-behavioral care, but the statement notes these could conceptually be extended to arrhythmia management and heart failure self-management.

The takeaway: reimbursement for cardiovascular AI is advancing but remains fragmented, and EP clinicians and health systems need to actively track code-specific coverage decisions and advocate for coherent payment models.

The Lifecycle: Monitoring, Drift, and Knowing When to Stop

Perhaps the most important section addresses what happens after deployment. The statement introduces a framework for continuous post-market surveillance:

Performance drift is a core concern, AI algorithm performance can degrade over time as patient populations, clinical practices, or data distributions shift. Healthcare systems should implement monitoring pipelines that track key performance indicators, input data characteristics, and model outputs with automated alerts for deviations outside predefined thresholds.

When performance issues are detected, three interventions are possible:

1. Retraining: Full model retraining on updated, representative datasets, controlled, validated, and potentially subject to regulatory oversight

2. Recalibration: Adjusting model parameters to align with current clinical reality without altering the model architecture

3. Cessation of use: If an AI model consistently demonstrates unacceptable performance, bias, or patient safety risks, it must be immediately stopped, with clear protocols for communicating findings to clinicians and regulators

The statement is emphatic: clinicians must maintain a critical perspective and never cede ultimate decision-making authority to an AI algorithm. Human oversight is non-negotiable.

Data Interoperability and Infrastructure

The paper outlines a practical data pipeline for interoperable AI in EP: EP data sources (invasive studies, CIEDs, remote monitoring, wearables, ECGs, EHR documentation) flow into EHR/FHIR platforms, through ETL pipelines into OMOP Common Data Model registries, then into AI development and validation environments, and finally into deployed EP workflows. The emphasis on bidirectional APIs, enabling AI outputs to be written back into the EHR in structured form rather than as static PDF reports, is a significant architectural recommendation.

Each AI prediction or recommendation should be linked to auditable identifiers enabling clinicians to retrieve the underlying signals and context. Models should be designed to function across multiple hardware vendors. Health system contracts should define how data is shared, how long data and model outputs are retained, and which party maintains compatibility as systems evolve.

Ethics and Human Rights

The conclusions anchor the entire framework in the World Health Organization's 2021 Ethics and Governance of Artificial Intelligence for Health guidance, which articulates six foundational principles: autonomy, well-being, transparency, accountability, inclusiveness, and sustainability. The statement calls for AI-enabled EP workflows to be designed and governed to protect patients while promoting equitable and sustainable health system benefits, implemented across borders with robust data protection, active combating of bias, system transparency, and clear accountability structures.

Why This Matters

This is the first time the Heart Rhythm Society has published a comprehensive, society-level framework addressing every stage of AI integration, from initial application design through regulatory approval, implementation, post-market monitoring, and potential cessation. For EP clinicians, it provides a structured decision-making process. For vendors and developers, it sets clear expectations for validation, interoperability, and transparency. For health systems, it establishes governance principles and practical infrastructure requirements.

The message is clear: AI in electrophysiology is no longer a future possibility, it's a present reality that requires systematic, responsible integration guided by evidence, ethics, and continuous oversight.

Download the Statement

Citation: Armoundas AA, Avari Silva JN, Baykaner T, et al. HRS Scientific Statement on Artificial intelligence integration framework into clinical electrophysiology workflows. Heart Rhythm. 2026. doi:10.1016/j.hrthm.2026.04.013