Exploration of Medicine

Table 1. Performance comparison of AI/ML vs. traditional methods in cardiovascular applications.

Clinical domain	AI/ML approach	Traditional method	AI/ML performance	Traditional performance	Improvement
Risk prediction
CVD risk assessment	Random forest/deep learning	Framingham risk score	AUC: 0.865	AUC: 0.765	+13.1%
Heart failure readmission	Gradient boosting	LACE score	AUC: 0.82	AUC: 0.69	+18.8%
Acute MI risk	Neural networks	TIMI score	Sensitivity: 94%	Sensitivity: 78%	+20.5%
Diagnostic accuracy
Arrhythmia detection	CNN (12-lead ECG)	Cardiologist review	Accuracy: 92.3%	Accuracy: 89.1%	+3.6%
Echo LV function	Deep learning	Manual measurement	Correlation: r = 0.98	Inter-observer: r = 0.89	+10.1%
Coronary stenosis	CNN (angiography)	Visual assessment	AUC: 0.91	AUC: 0.84	+8.3%
Workflow efficiency
Echo analysis time	Automated AI	Manual analysis	5 minutes	45 minutes	–88.9%
CT calcium scoring	AI algorithm	Manual scoring	30 seconds	10 minutes	–95.0%
ECG interpretation	Real-time AI	Physician review	10 seconds	5 minutes	–96.7%

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AI: artificial intelligence; CNN: convolutional neural network; ML: machine learning.

Declarations

Author contributions

VS: Conceptualization, Investigation, Methodology, Writing—original draft, Writing—review & editing, Project administration. GKC: Investigation, Data curation, Writing—original draft, Writing—review & editing. SK: Investigation, Formal analysis, Writing—review & editing. KA: Investigation, Validation, Writing—review & editing. SS: Investigation, Writing—review & editing, Supervision. All authors have reviewed, discussed, and agreed to their individual contributions. All authors read and approved the final submitted version of the manuscript.

Conflicts of interest

The authors declare that they have no conflicts of interest. There are no personal, professional, or financial relationships that could potentially be construed as a conflict of interest with respect to this manuscript. No author has any financial or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

All datasets analyzed for this study are included in the manuscript. This review is based on publicly available published literature. The search strategies, inclusion/exclusion criteria, data extraction forms, and all analyzed data are provided within the manuscript. No additional unpublished data were generated for this study. All source materials are properly cited and can be accessed through the references provided.

Funding

Not applicable.

Copyright

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