Editorial: Novel translational advances in artificial intelligence for diagnosis and treatment of cardiovascular diseases

Abstract

Artificial Intelligence (AI) is poised to rapidly evolve medical practice through novel discoveries using deep learning (DL), large language models (LLM) and other forms of generative AI. 1 These AI techniques are currently able to interpret and summarize data from immense data fields, where they can enhance image reconstruction, reduce noise, and assist in the interpretation of complex datasets. 2 There have been several pioneering AI applications in cardiology. Myocardial perfusion imaging (MPI), such as single photon emission computed tomography (SPECT) and positron emission tomography (PET), is used in disease diagnosis and risk assessment. AI application to SPECT or PET for coronary artery disease (CAD) has led to improved diagnostic accuracy, risk stratification, and therapeutic decision-making. 2 Attia et al. developed an AI-enabled electrocardiograph (ECG) using a convolutional neural network that was able to detect the electrocardiographic signature of atrial fibrillation (AF) present during normal sinus rhythm using standard ECG leads. 3 Another large study by Hannun et al., using a deep learning approach and ECG, found a similar successful detection ability for AF using AI which was better than the AF detection rate for physicians. 4 A similar AI approach using an ECG was found to be highly successful at detecting dilated cardiomyopathy (DCM). 5 A randomized controlled trial conducted to examine whether AI-guided assessment of cardiomyopathy was similar or different to sonographers and cardiologists using echocardiography could not distinguish between the two methods, with the advantage that the AIguided workflow saved time for sonographers and cardiologists. 6 Interestingly, AI methods to detect cardiomyopathy have found that patients with so-called 'false positives' are at greater risk of poor cardiovascular outcomes later, concluding that AI-models may be good at detecting potential cardiovascular issues in the future over current methods. 7 With these successes, AI is increasingly likely to be used to detect, diagnose and predict current and future cardiovascular events or poor outcomes. There are numerous impacts to incorporating AI into patient care including altering staffing levels, changing which equipment is used (i.e., ECG vs. echocardiogram), saving time, reducing costs, and improving outcomes. Additionally, AI is leading to major innovations in clinical practice. The possibilities are endless, with the potential to integrate multiple imaging technologies and interpret findings based on sex, race and biomarker data to improve prediction models. 1 The manuscripts included in this Research Topic provide further contributions to the field on this important topic (

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