Exploration of Medicine

Table 2. FDA-approved AI/ML applications in cardiovascular medicine.

Application category	Device/Algorithm	FDA approval date	Clinical indication	Performance metrics	Clinical impact
ECG interpretation
Arrhythmia detection	Apple Watch ECG	2018	Atrial fibrillation screening	Sensitivity: 98.3%, specificity: 99.6%	Population screening
LV dysfunction	AI-ECG (Mayo Clinic)	2019	Left ventricular dysfunction detection	AUC: 0.93, sensitivity: 86.3%	Early diagnosis
12-Lead analysis	Cardiologs ECG Review	2017	Multi-arrhythmia detection	Sensitivity: > 90% for major arrhythmias	Automated interpretation
Cardiac imaging
Echocardiography	EchoGo Core	2020	Automated measurements	< 2% variability vs. expert	Workflow efficiency
Cardiac CT	HeartFlow FFRCT	2014	Fractional flow reserve	Diagnostic accuracy: 84%	Non-invasive assessment
Cardiac MRI	Circle CVI42	2019	Ventricular function analysis	Inter-observer variability < 5%	Standardized analysis
Risk stratification
CAD risk	Cleerly Plaque Analysis	2021	Coronary plaque quantification	Risk prediction improvement: 15–20%	Precision medicine
Heart failure	IBM Watson Health	2020	HF readmission prediction	AUC: 0.82	Resource optimization

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AI: artificial intelligence; 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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