Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare

Abstract

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF)

“…With appropriate training and education for HCPs, it will enable a stepwise framework to incorporate high-value AI into routine cardiovascular care. 18 …”

“… 16 This platform was installed on a virtual machine hosted on Amazon Web Services (AWS) in the AWS Europe (London) region, with infrastructure developed for the RATE-AF trial by the BigData@Heart consortium. 17 , 18 Researchers were granted access to the Fitbit intraday developer application which allowed automated data collection for registered participants through the RADAR-base platform.…”

Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices

“…The laboratory’s stepwise approach to AI intelligence begins with the selection and pre-processing of initial data, followed by careful consideration of the appropriate learning algorithm, and finally, thorough evaluation and validation of the obtained results [ 29 ]. Even if clinical laboratory experts are not responsible for developing AI algorithms, they can help guide the selection process by evaluating algorithms based on medical and biological data [ 30 ]. For substantial implementation to occur, lab professionals in Clinical Chemistry must eventually adopt AI tools.…”

Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome

“…classification, regression), in order to perform this task on new data. 5 , 6 Machine learning has shown superior results to predict mortality in patients with CCS using data from coronary computed tomographic angiography (CCTA) or stress cardiac magnetic resonance (CMR) compared with traditional methods. 7 , 8 Despite these promising results, limited ML studies have used TTE data to predict mortality in patients with CCS.…”