This article is based on an interview with Dr Lin Yee Chen, Director of the Lillehei Heart Institute at the University of Minnesota Medical School, conducted for Heart Failure Academy.
Key Take-home Messages
- AI-enabled ECG tools are demonstrating strong performance in identifying asymptomatic left ventricular systolic dysfunction (LVSD) before overt heart failure develops (Rhee et al., 2026).
- Prediction of future atrial fibrillation may represent one of the most promising emerging applications of cardiovascular AI (Chen, 2026).
- Validation, calibration, bias assessment and explainability remain critical prerequisites before widespread clinical adoption (Chen, 2026; Venturiello et al., 2026).
Trust in AI will depend not only on performance, but also on transparency, clinical oversight and real-world utility (Chen, 2026; Venturiello et al., 2026).
From Innovation to Implementation
Artificial intelligence is rapidly moving from research environments into everyday cardiovascular care, yet relatively few applications have established a sufficiently robust evidence base to support routine clinical implementation.
According to Dr Lin Yee Chen, ECG-based AI has emerged as one of the most mature cardiovascular AI applications currently approaching broader clinical adoption. Unlike many AI tools that remain confined to research settings, AI-enhanced ECG algorithms are increasingly being evaluated as practical screening instruments capable of identifying disease before symptoms become apparent (Chen, 2026; Venturiello et al., 2026).
For Dr Chen, one of the most promising opportunities is earlier identification of patients progressing towards heart failure, creating a window for intervention before clinical deterioration occurs (Chen, 2026).
Detecting Silent Ventricular Dysfunction
One of the best-studied applications of AI-ECG is the detection of asymptomatic left ventricular systolic dysfunction, a recognised precursor of symptomatic heart failure (Venturiello et al., 2026).
Traditional screening strategies depend on echocardiography, which is highly informative but resource intensive. AI-ECG seeks to identify electrical signatures associated with ventricular dysfunction using a routinely acquired ECG, potentially enabling broader and earlier detection (Venturiello et al., 2026).
Evidence supporting this approach continues to grow. Recent studies have demonstrated excellent diagnostic performance for AI-ECG models in identifying asymptomatic LVSD, raising the possibility that ECG-based screening could serve as a gatekeeper for more resource-intensive imaging and earlier initiation of guideline-directed therapies (Rhee et al., 2026; Venturiello et al., 2026).
According to Dr Chen, some centres are already exploring the use of AI-ECG to identify previously unrecognised ventricular dysfunction, creating opportunities for earlier intervention. However, important questions remain regarding how these tools perform across diverse populations and whether earlier detection ultimately translates into improved long-term outcomes (Chen, 2026; Venturiello et al., 2026).
Atrial Fibrillation Prediction May Be the Next Frontier
Beyond ventricular dysfunction, Dr Chen identified prediction of future atrial fibrillation as one of the most promising emerging applications of cardiovascular AI (Chen, 2026).
AF and heart failure frequently coexist and often worsen one another. Detecting patients at elevated risk before arrhythmia develops could create opportunities for earlier monitoring, risk factor modification and intervention (Chen, 2026).
Rather than relying solely on overt rhythm abnormalities, Dr Chen highlighted markers that reflect underlying atrial disease (Chen, 2026):
- P-wave duration
- P-wave axis
- P-wave terminal force in lead V1
- Evidence of interatrial block
Collectively, these measurements may provide insight into atrial remodelling, atrial cardiomyopathy and broader cardiovascular risk (Chen, 2026).
While enthusiasm for AF prediction is increasing, future studies will need to clarify how clinicians should respond when AI models identify elevated risk in patients without documented arrhythmia (Chen, 2026).
Why Validation Matters More Than Accuracy
Despite growing excitement around cardiovascular AI, Dr Chen cautioned against equating impressive performance metrics with clinical readiness (Chen, 2026).
"A model may demonstrate excellent discrimination, but if predicted probabilities do not align with observed outcomes, clinical usefulness becomes limited" (Chen, 2026).
Before adopting an AI tool in routine practice, Dr Chen believes four key requirements should be met (Chen, 2026).
Generalisability
Models should demonstrate consistent performance across healthcare systems, geographic regions and patient populations (Chen, 2026; Venturiello et al., 2026).
Calibration
Predicted risk estimates should closely match observed event rates and support meaningful clinical decision-making (Chen, 2026).
Bias Assessment
Performance should be evaluated across relevant demographic and clinical subgroups (Chen, 2026; Venturiello et al., 2026).
Explainability
Clinicians need confidence that model outputs are grounded in meaningful physiological signals and can be interpreted within existing clinical frameworks (Chen, 2026; Venturiello et al., 2026).
"Explainable AI is critical for building physician confidence" (Chen, 2026).
Which Applications Will Reach Routine Practice?
Looking ahead, Dr Chen expects AI-ECG screening tools to become increasingly embedded within cardiovascular care pathways over the next several years (Chen, 2026).
Detection of asymptomatic LV systolic dysfunction appears particularly well positioned for broader adoption because it is supported by a growing evidence base and a clear downstream pathway involving confirmatory imaging and established therapies (Rhee et al., 2026; Venturiello et al., 2026).
Similarly, Dr Chen anticipates wider use of tools capable of identifying patients at increased risk of developing atrial fibrillation before symptoms emerge (Chen, 2026).
By contrast, AI systems designed to predict individual responses to specific heart failure therapies are likely to remain investigational until further validation is available (Chen, 2026; Venturiello et al., 2026).
The Unexpected Winner: Documentation AI
Interestingly, when asked which AI application currently offers the clearest practical benefit, Dr Chen did not choose a diagnostic algorithm (Chen, 2026).
Instead, he highlighted AI-assisted clinical documentation. Ambient AI systems capable of generating draft consultation notes during patient encounters may reduce administrative burden and improve workflow efficiency, allowing clinicians to devote more time to patient care (Chen, 2026).
However, Dr Chen emphasised that physician oversight remains essential to ensure accuracy, completeness and accountability (Chen, 2026).
Human Oversight Remains Essential
Despite his enthusiasm for selected applications, Dr Chen repeatedly returned to the need for human oversight (Chen, 2026).
"The biggest risk is over-reliance" (Chen, 2026).
For that reason, he views AI as a tool to augment—not replace—clinical judgement. As cardiovascular AI moves from innovation to implementation, the challenge may be less about developing increasingly sophisticated algorithms and more about ensuring clinicians understand when to trust them, how to validate them and where their limitations lie (Chen, 2026; Venturiello et al., 2026).
References
Chen LY (2026) AI and Heart Failure: What Is Truly Ready for Clinical Use? Heart Failure Academy.
Rhee TM, Kang S, Lee MS, et al. (2026) Artificial Intelligence–Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population. JACC Advances, 5(4):102660.
Venturiello D, Vignaroli W and Nasso G (2026) AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways. Open Heart, 13(2):e004211.