Artificial intelligence has the potential to transform the lives of patients. The impacts are far ranging and extend from how whole medical systems are organized, through identification of new drugs and disease classifications, to optimization of management decisions for individual patients. Over the last few years significant technical developments in artificial intelligence methodology have expanded its power to address health questions and use data. These technical advances have been supported by numerous proof-of-principle demonstrations of potential clinical utility. Now, one of the most exciting, and most challenging, areas of research is identification of the advances that show sufficient promise to be translated into clinical care within cardiology.
This Research Topic will bring together reviews, opinion pieces and original research articles on the topic of ‘Translating artificial intelligence into clinical use within cardiology’. The goal will be to highlight the key applications and technical developments within cardiology, cardiovascular medicine, primary care and cardiothoracic surgery that incorporate artificial intelligence and are closest to, or have already successfully translated, into clinical use. The articles will have the opportunity to present standards for clinical evidence required for clinical translation including reports of clinical trial results and descriptions of trial designs as well as in silico validation and replication studies. Articles that address patient and medical perspectives on artificial intelligence and how to take these into account to ensure artificial intelligence is explainable for the medical profession will also be an important part of the research topic.
Specific themes we would like contributors to address include, but are not limited to:
1) Original research articles, descriptive and systematic reviews and opinion pieces focused on translation of artificial intelligence into clinical cardiology practice.
2) To cover artificial intelligence as applied to imaging, decision support and diagnostics as well as drug and disease discovery, augmented/virtual reality and healthcare system operations.
3) In particular, priority will be given to reports of clinical trial results, full protocols of planned trials or proposals for trial designs related to testing of artificial intelligence in healthcare.
4) Appropriate data uses including ethical considerations as well as testing of different models for data use will be included.
5) Articles, either as reviews or original articles, that highlight the most promising, or already successful, clinical applications are encouraged
Conflict of Interest Statement:
Prof. Paul Leeson: Founder and Non-Executive Director of Ultromics - an AI-imaging company, inventor on patents in the field of AI and echocardiography, research grants from Lantheus Medical Imaging, previous consultancy for Intelligent Ultrasound.
Prof. Pablo Lamata is member of the scientific advisory board at Ultromics, and inventor of patents in the field of AI applied to cardiovascular flow.
Artificial intelligence has the potential to transform the lives of patients. The impacts are far ranging and extend from how whole medical systems are organized, through identification of new drugs and disease classifications, to optimization of management decisions for individual patients. Over the last few years significant technical developments in artificial intelligence methodology have expanded its power to address health questions and use data. These technical advances have been supported by numerous proof-of-principle demonstrations of potential clinical utility. Now, one of the most exciting, and most challenging, areas of research is identification of the advances that show sufficient promise to be translated into clinical care within cardiology.
This Research Topic will bring together reviews, opinion pieces and original research articles on the topic of ‘Translating artificial intelligence into clinical use within cardiology’. The goal will be to highlight the key applications and technical developments within cardiology, cardiovascular medicine, primary care and cardiothoracic surgery that incorporate artificial intelligence and are closest to, or have already successfully translated, into clinical use. The articles will have the opportunity to present standards for clinical evidence required for clinical translation including reports of clinical trial results and descriptions of trial designs as well as in silico validation and replication studies. Articles that address patient and medical perspectives on artificial intelligence and how to take these into account to ensure artificial intelligence is explainable for the medical profession will also be an important part of the research topic.
Specific themes we would like contributors to address include, but are not limited to:
1) Original research articles, descriptive and systematic reviews and opinion pieces focused on translation of artificial intelligence into clinical cardiology practice.
2) To cover artificial intelligence as applied to imaging, decision support and diagnostics as well as drug and disease discovery, augmented/virtual reality and healthcare system operations.
3) In particular, priority will be given to reports of clinical trial results, full protocols of planned trials or proposals for trial designs related to testing of artificial intelligence in healthcare.
4) Appropriate data uses including ethical considerations as well as testing of different models for data use will be included.
5) Articles, either as reviews or original articles, that highlight the most promising, or already successful, clinical applications are encouraged
Conflict of Interest Statement:
Prof. Paul Leeson: Founder and Non-Executive Director of Ultromics - an AI-imaging company, inventor on patents in the field of AI and echocardiography, research grants from Lantheus Medical Imaging, previous consultancy for Intelligent Ultrasound.
Prof. Pablo Lamata is member of the scientific advisory board at Ultromics, and inventor of patents in the field of AI applied to cardiovascular flow.
