By Dr. Chinta SidharthanReviewed by Susha Cheriyedath, M.Sc.Jun 25 2024 In a recent ‘State-Of-The-Art Review’ published in the Journal of the American College of Cardiology, scientists reviewed current developments in artificial intelligence -based innovations such as diagnostic, biomarker detection, and prognostic tools that are improving the quality, precision, and efficacy of multimodal cardiovascular care in the areas of biomedical discovery, as well as clinical practice.
Additionally, with new computational methods being developed rapidly and healthcare data being digitized, AI tools are being used extensively to monitor and study patterns in human diseases. Especially in cardiovascular medicine, AI-based tools are now being used not just to automate workflows but also in learning models to connect different data types and systems and generate inferences and knowledge.
AI-based innovations in cardiovascular healthcare Machine learning and AI-based tools are effective in processing unstructured, raw data of biometric images and signals to determine phenotypic variations and predict disease risk. One example of AI's effectiveness in risk screening is the interpretation of electrocardiograms. The AI-guided process uses electrocardiograms in sinus rhythm to screen for paroxysmal atrial fibrillation.
Moreover, the review discusses how AI enables opportunistic screening of cardiovascular diseases using common diagnostic tools like chest X-rays, thus broadening the scope and reach of cardiovascular diagnostics. The prognostic role of AI-based tools also extends to determining how specific therapies in individuals may impact the outcomes. For example, AI-based tools can integrate multimodal data to discriminate the risk of non-arrhythmic versus arrhythmic mortality in cases where implantable cardioverter-defibrillators are recommended despite the heterogeneous risk.
AI-assisted technologies are already being used to standardize the assessment and documentation process, maximizing the direct clinical care available to the patients. These tools can potentially also be used for the grading and characterization of cardiovascular recordings, accelerating population-wide screening. The decreased need for trained individuals to use AI-based diagnostic tools can also democratize and improve access to high-quality cardiovascular diagnosis and clinical care.