Key result
Multimodal AI approaches frequently improved prediction performance over single-modality approaches in ECG-based cardiovascular disease classification, with many studies reporting gains of 2-6%.
Why the study?
Despite advances in AI for ECG analysis, limitations persist regarding dataset diversity, class imbalance, rare condition underrepresentation, lack of external validation, limited model interpretability, computational expense, and constrained multimodal integration.
Does multimodal AI improve prediction over single-modality approaches in ECG-based cardiovascular disease classification?
Systematic Review (n=227)
Does multimodal AI improve prediction over single-modality approaches in ECG-based cardiovascular disease classification?
Effect estimate: 2-6% gain
Multimodal AI approaches in ECG-based cardiovascular disease classification frequently improve performance by 2-6% compared to single-modality methods, though challenges in interpretability and validation remain.
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Karandikar et al. (2026) conducted a systematic review in Cardiovascular disease (n=227). Multimodal AI and advanced architectures vs. Single-modality approaches and traditional methods was evaluated on Prediction performance (2-6% gain). Multimodal AI approaches frequently improved prediction performance over single-modality approaches in ECG-based cardiovascular disease classification, with many studies reporting gains of 2-6%.
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