Translational research is at the forefront of advances in applying artificial intelligence (AI) to improve the diagnosis and treatment of cardiovascular diseases (CVDs). Clinicians, basic researchers and data scientists are working together crossing boundaries between fields in order to bring advances to this rapidly developing area of research. Many practical and theoretical applications are needed to enable AI to significantly improve and change the method that physicians and other hospital staff use in the diagnosis and treatment of patients with CVD. Advances in AI will augment and accelerate scientific discovery to reduce morbidity and mortality from CVD. It is critical that researchers develop innovative solutions to our most pressing challenges in CVD and consider possible negative outcomes and challenges present in this technology.
Cardiology is at the forefront of the AI revolution in medicine because AI has worked well to accurately predict cardiac outcomes and examine non-invasive parameters. As leaders the field is responsible for providing leadership and innovation on ethical safeguards that need to be set in place and issues related to data management. AI is the most accurate when it is trained on big data, but what its role will be in rare diseases or unusual cases is yet to be determined. However, it is clear that AI will have a major impact on how cardiovascular medicine is practiced in the future.
This Research Topic aims to highlight novel advances in translating artificial intelligence tools and methods into clinical practice, particularly related to cardiovascular medicine. Articles that examine basic and translational research are welcomed as well as articles describing the more theoretical issues of the topic. We encourage both original research and review articles. Articles can include, but are not limited to, the following topics:
• The future potential of AI as it relates to cardiovascular medicine;
• Translational approaches to advance AI in healthcare and cardiovascular medicine;
• Theories related to the impact of AI on medical practice;
• Obstacles tat to be overcome to use AI effectively in diagnosis of CVDs;
• Obstacles to be overcome to use AI effectively for the treatment of CVDs;
• Potential or new tools being developed for AI drug discovery related to CVDs;
• Potential or new tools being developed using AI to prevent adverse outcomes from therapies ranging from surgery to drug therapy;
• Managing perception of AI with patients and how this effects clinical outcomes;
• Translational advances in tools and methods of AI applied to CVDs;
• Translational science approaches that cross boundaries between disciplines to promote AI innovation;
• Benefits and challenges associated with AI approaches to interpreting basic and clinical big data from omics or other sources;
• Challenges associated with AI interpretation of findings derived from training models of vast amounts of unlabeled data or data of poor quality.
Translational research is at the forefront of advances in applying artificial intelligence (AI) to improve the diagnosis and treatment of cardiovascular diseases (CVDs). Clinicians, basic researchers and data scientists are working together crossing boundaries between fields in order to bring advances to this rapidly developing area of research. Many practical and theoretical applications are needed to enable AI to significantly improve and change the method that physicians and other hospital staff use in the diagnosis and treatment of patients with CVD. Advances in AI will augment and accelerate scientific discovery to reduce morbidity and mortality from CVD. It is critical that researchers develop innovative solutions to our most pressing challenges in CVD and consider possible negative outcomes and challenges present in this technology.
Cardiology is at the forefront of the AI revolution in medicine because AI has worked well to accurately predict cardiac outcomes and examine non-invasive parameters. As leaders the field is responsible for providing leadership and innovation on ethical safeguards that need to be set in place and issues related to data management. AI is the most accurate when it is trained on big data, but what its role will be in rare diseases or unusual cases is yet to be determined. However, it is clear that AI will have a major impact on how cardiovascular medicine is practiced in the future.
This Research Topic aims to highlight novel advances in translating artificial intelligence tools and methods into clinical practice, particularly related to cardiovascular medicine. Articles that examine basic and translational research are welcomed as well as articles describing the more theoretical issues of the topic. We encourage both original research and review articles. Articles can include, but are not limited to, the following topics:
• The future potential of AI as it relates to cardiovascular medicine;
• Translational approaches to advance AI in healthcare and cardiovascular medicine;
• Theories related to the impact of AI on medical practice;
• Obstacles tat to be overcome to use AI effectively in diagnosis of CVDs;
• Obstacles to be overcome to use AI effectively for the treatment of CVDs;
• Potential or new tools being developed for AI drug discovery related to CVDs;
• Potential or new tools being developed using AI to prevent adverse outcomes from therapies ranging from surgery to drug therapy;
• Managing perception of AI with patients and how this effects clinical outcomes;
• Translational advances in tools and methods of AI applied to CVDs;
• Translational science approaches that cross boundaries between disciplines to promote AI innovation;
• Benefits and challenges associated with AI approaches to interpreting basic and clinical big data from omics or other sources;
• Challenges associated with AI interpretation of findings derived from training models of vast amounts of unlabeled data or data of poor quality.