Artificial Intelligence (AI) is revolutionizing cardiac imaging across the patient journey, offering transformative potential in efficiency, accuracy, and personalized care. The integration of AI spans various steps in the cardiac imaging value chain, necessitating a comprehensive understanding of its applications. The American Heart Association released a scientific statement on this.
The efficacy of AI in each step relies on robust data for training and testing models. The era of big data facilitates the examination of large datasets, enhancing AI development. National Institutes of Health policies reinforce data-sharing commitments, promoting transparency and collaboration.
AI contributes to decision support in clinical presentations, automating outpatient visit interpretation using Natural Language Processing of Electronic Medical Records. Inpatient care benefits from AI identifying "red flags" for potential patient deterioration. AI aids in choosing optimal imaging tests, considering risk factors and medical history. Automation enhances patient flow, billing, and documentation through robotic path automation, streamlining repetitive administrative tasks.
Automation extends to image acquisition, with AI guidance improving cardiac CT settings, patient positioning, and dose modulation. AI analysis improves electrocardiographic tracing interpretation by minimizing motion artifacts. AI tools excel in image interpretation and quantitative analysis, automating landmark identification and enabling coronary calcium scoring.
The outputs of image AI offer the potential for additional risk stratification. AI identifies health-to-disease spectra in heart failure patients, aiding personalized management. Aortic stenosis diagnosis and clinical decision support benefit from AI automation, aligning with established clinical guidelines.
Human trust in AI outputs is crucial for effective implementation. Explainability metrics enhance AI trustworthiness, balancing transparency with intellectual property protection. Training datasets quality directly influences AI accuracy, emphasizing the need for balanced, high-quality datasets and robust de-identification processes.
AI's integration in cardiac imaging unlocks value at every patient-care step. Collaborative efforts, ethical frameworks, and continuous improvements in AI technologies are imperative for realizing the full potential of AI in cardiac imaging, ensuring enhanced patient outcomes and streamlined clinical practices.
