What is the clinical evidence from this study?

Study design: Systematic Review. Population: Cardiovascular diagnosis and risk prediction (n=108). Intervention: Artificial intelligence-enabled electrocardiogram (ECG) analysis.

What question did this study set out to answer?

To systematically assess the evidence base, methodological quality, and clinical readiness of artificial intelligence algorithms used in electrocardiogram interpretation for cardiovascular diagnosis and risk prediction.

August 29, 2026BMC Cardiovascular DisordersOpen Access

Artificial intelligence in electrocardiogram interpretation for cardiovascular diagnosis and risk prediction: a systematic review of evidence through May 2026

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Key result

AI-enabled ECG interpretation excels at arrhythmia detection while structural disease screening remains less mature.

n=108

Why the study?

AI-enabled ECG analysis has expanded across cardiovascular diagnosis, monitoring, and risk prediction, but published studies vary in methodology, validation, and clinical applicability.

Does artificial intelligence-enabled electrocardiogram analysis improve cardiovascular diagnosis and risk prediction?

Population

108 original human studies evaluating AI, machine learning, or deep learning ECG applications

Design

Systematic review following PRISMA 2020 with narrative synthesis

Authors

MEMohamed ElhussainInterventional / Structural Cardiology EAEsra Abdalla RARagda AliUniversity of Khartoum

Discussion

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Implication

AI-ECG evidence remains heterogeneous in validation and applicability; leaves open standardized clinical use pending prospective trials.

Key Points

To systematically assess the evidence base, methodological quality, and clinical readiness of artificial intelligence algorithms used in electrocardiogram interpretation for cardiovascular diagnosis and risk prediction.
Searched PubMed and Cochrane Library through May 2026 for human studies evaluating machine learning and deep learning applications in ECG diagnosis, monitoring, and risk prediction.
Evaluated risk of bias using the PROBAST tool and conducted a narrative synthesis across 108 included studies due to extensive methodological and clinical heterogeneity.
Arrhythmia detection, particularly for atrial fibrillation, represented the most mature, validated, and frequently evaluated clinical application domain.
Studies utilizing benchmark datasets reported high apparent diagnostic performance, whereas externally validated studies and larger clinical cohorts yielded more moderate, clinically credible estimates.
Frequent methodological shortcomings included lack of external validation, risk of model overfitting, unclear train-test data separation, class imbalance, and inadequate reporting of calibration.

Study Design

Type

Systematic Review (n=108)

Structured PICO

Does artificial intelligence-enabled electrocardiogram analysis improve cardiovascular diagnosis and risk prediction?

Population

108 studies evaluating artificial intelligence, machine learning, or deep learning applications in ECG-based cardiovascular detection, classification, diagnosis, monitoring, or risk prediction.

Exposure

Artificial intelligence-enabled electrocardiogram (ECG) analysis

Outcome

Performance of AI in cardiovascular diagnosis, monitoring, and risk prediction

AI-enabled ECG interpretation has the strongest evidence base for arrhythmia detection, while applications for structural disease screening and prognostic modeling require more rigorous, externally validated prospective studies.

Limitations

Limited external validation
Overfitting risk
Unclear train-test separation
Class imbalance
Incomplete reporting of calibration and robustness
limited external validation
overfitting risk
unclear train-test separation
class imbalance
incomplete reporting of calibration and robustness

Cite This Study

Elhussain et al. (2026) conducted a systematic review in Cardiovascular diagnosis and risk prediction (n=108). Artificial intelligence-enabled electrocardiogram (ECG) analysis was evaluated. Across 108 studies, AI-enabled ECG interpretation showed the strongest support for arrhythmia detection and automated classification, while structural disease screening remained less mature.

synapsesocial.com/papers/6a9299338e5d7d1fc0c110a8 https://doi.org/10.1186/s12872-026-06234-5

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Artificial intelligence in electrocardiogram interpretation for cardiovascular diagnosis and risk prediction: a systematic review of evidence through May 2026

Discussion

Member takes

Implication

Key Points

Study Design

Structured PICO

Limitations

Cite This Study

Artificial intelligence in electrocardiogram interpretation for cardiovascular diagnosis and risk prediction: a systematic review of evidence through May 2026

Discussion

Member takes

Implication

Key Points

Study Design

Structured PICO

Limitations

Cite This Study

Also Consider

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