AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
Keyphrases
- artificial intelligence
- computed tomography
- machine learning
- cardiovascular disease
- dual energy
- deep learning
- coronary artery
- healthcare
- big data
- image quality
- quality improvement
- palliative care
- magnetic resonance imaging
- positron emission tomography
- magnetic resonance
- case report
- metabolic syndrome
- heart rate variability
- heart rate
- pulmonary arterial hypertension