Key result
AI algorithms enhance MACE prediction precision over traditional risk models to enable personalized treatment.
Design
Review
May enable personalized cardiac care now; extends traditional risk models but leaves open outcome benefit pending prospective validation.
Artificial Intelligence has enabled data-driven diagnostic, prognostic, and therapeutic applications in cardiovascular medicine. Contemporary machine learning algorithms improve the sensitivity and specificity of ECG interpretation, echocardiography quantification, and coronary imaging by automating feature extraction and reducing inter-observer variability. Deep neural networks, which are trained on multimodal datasets involving clinical records, biomarkers, genomics, and wearable sensor data, are able to predict major adverse cardiac events with much higher precision compared to traditional risk models. AI-enabled decision support systems allow personalized treatment strategies for heart failure, coronary artery disease, and arrhythmias by projecting an individual response to pharmacological or interventional therapies. Integration with robotic-assisted procedures and electrophysiology mapping enhances procedural precision. Limitations persist with limited dataset diversity, regulatory hurdles, model interpretability, and the need for prospective clinical validation. Notwithstanding such limitations, AI is a very important catalyst in the evolution of precision cardiology.
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Ms. Nashra Usmani*1, Ms. Shaikh Fiza2, Prof. Jagruti Patil3 (2026) studied this question. AI algorithms significantly enhance the precision of predicting major adverse cardiac events compared to traditional risk models, enabling personalized treatment strategies.
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