The advancement of artificial intelligence (AI) technology has enabled new possibilities for cardiovascular health care and medicine. Generative AI models, such as GPT-4, DALL-E 2, and Med-PALM 2, have the potential to revolutionize the way cardiovascular health care is delivered.

This call for papers aims to explore the potential of generative AI in health care and medicine, specifically in the field of cardiovascular medicine and its subspecialties. This includes, but is not necessarily limited only to, large language models and multimodal AI that can be applied to electrophysiology, congenital heart diseases, transplant cardiology, and precision cardiology.

Submissions may address topics such as automated disease prevention, risk prediction or stratification, cardiovascular diagnosis and monitoring, cardiovascular imaging, personalized care or treatment, care planning and management, drug discovery, and event detection. We especially welcome original paper submissions that robustly examine the comparative effectiveness of generative AI technologies applied to cardiovascular disciplines, or viewpoints that offer a well-argued and evidence-based contrary view of generative AI applications in cardiovascular medicine. To be considered for publication in the special issue, authors must provide evidence of the relevance of their research to the topic of this call for papers.

Submissions are invited on, but not limited to, the following topics:

Applying AI, including generative AI and multimodal or large language models, to enhance cardiovascular testing and imaging, assist in diagnosis and treatment, and aid with the development and monitoring of care management plans
Applying AI to improve risk stratification and prediction of cardiovascular events, including focusing on sex- and gender-based differences in cardiovascular diseases or maternal and perinatal cardiovascular health
Delivering personalized, accessible cardiac care using AI-powered chatbots
Applying AI to improve patient engagement or adherence and health care outcomes in cardiac rehabilitation, post-stroke rehabilitation, and rehabilitation after cardiovascular-related procedures
Generative AI, multimodal or large language model technologies for cardiovascular disease knowledge representation and reasoning, including the use of knowledge graphs and semantic web technologies to store and process large amounts of structured and unstructured data
Neural machine translation to generate natural language responses from structured cardiovascular pharmacologic, physiologic, medical, and clinical data
AI-driven computer vision algorithms applied to all forms of cardiovascular testing and imaging, including but not limited to electrophysiologic testing, echocardiograms, computed tomography, magnetic resonance imaging, nuclear imaging, and other types of novel cardiovascular imaging
Detecting and analyzing cardiovascular events in real time using mobile, wearable, or environmental sensor data
Improving clinical workflows and care pathways using generative or AI technologies based on organizational data, medical or insurance claims data, or other large health care datasets
Accelerating drug discovery and finding new therapeutics using generative AI
Ethic, legal, and social issues and implications for clinical applications of generative AI technologies in cardiovascular care
Limitations and challenges of applying generative AI technologies to cardiovascular health and medicine, including structural or systemic bias, health disparities, marginalization of patient populations who are routinely underrepresented, and secondary use of data or algorithms
Need for data infrastructures, data security and regulations, and applicable principles (eg, FAIR guiding principles) to enable generative AI applications in routine cardiovascular care
Viewpoints on the future of AI-driven digital cardiovascular health care and medical practices.

JMIR Cardio welcomes original, unpublished submissions from researchers and practitioners in medicine, health care, computer science, and related fields. We invite submissions of original research papers, literature reviews, and viewpoints. We encourage authors to submit preprints for peer review in JMIR Cardio. We also encourage submissions that address practical challenges and opportunities related to the use of generative AI in digital cardiovascular medicine.

All submissions will undergo a rigorous peer-review process, and accepted articles will be published as part of a special issue on Generative and Multimodal AI in Digital Cardiovascular Medicine.

We will not accept articles that are solely written by ChatGPT or other generative AI technologies or services applied to writing text. Please refer to our emerging editorial policy regarding the use of AI writing tools in article ideation or manuscript preparation, and specifically on how to disclose the use of generative tools in the manuscript. If original generated text is used (eg, ChatGPT-produced content), it should be presented as a quote, textbox, or figure. In particular, we ask to keep on file the complete original transcripts that include the full human prompts and AI-generated responses. These transcripts must be submitted as Multimedia Appendices (or supplementary material) along with the manuscript.

To submit an article to this JMIR theme issue, please go here and select the journal section entitled “Generative and Multimodal AI in Digital Cardiovascular Medicine."

All articles submitted to this theme issue will be shared and published rapidly through the following mechanisms:

All peer-reviewed articles in this theme issue will be immediately and permanently made open access. This is the standard for all titles within the JMIR Publications portfolio.
Articles can be made immediately available in JMIR Preprints (with a DOI) after submission if authors select the preprint option at submission to enable this service.

Submission deadline: No deadline. This is an open call for submissions.

Submissions not reviewed or accepted for publication in this JMIR Cardio theme issue (e-collection) may be offered cascading peer review or transfer to other JMIR journals, according to standard JMIR Publications policies. For example, highly technical papers may be transferred or submitted to JMIR Biomedical Engineering. Selected submissions also may be in scope for JMIR AI. Submissions focused on generative AI technologies in medical education may be better suited for the JMIR Medical Education theme issue on "ChatGPT, Generative Language Models and Generative AI in Medical Education.”

Early-stage formative work that informs the design of future interventions or research may better fit the scope for JMIR Formative Research. Authors are encouraged to submit study protocols or grant proposals to JMIR Research Protocols before data acquisition to pre-register the study (Registered Reports - subsequent acceptance in one of the JMIR Publications journals is then guaranteed).

This paper is in the following e-collection/theme issue:

Digital Health Innovation: A Toolkit to Navigate From Concept to Clinical Testing

Digital Health Innovation: A Toolkit to Navigate From Concept to Clinical Testing