Advances in Cardioneurology: AI and Machine Learning for Prediction, Prevention, and Personalized Care

Cardioneurology sits at the center of some of today’s highest-burden clinical problems, where cardiovascular disease drives neurological injury and neurological disease worsens cardiovascular risk. Stroke, cognitive impairment, autonomic dysfunction, hypoxic–ischemic brain injury, and peri-procedural neurological complications remain common, costly, and often preventable, yet prediction and prevention in real-world care still lag behind what current data streams can support.

At the same time, healthcare now generates continuous, high-dimensional signals across imaging, electrophysiology, vital signs, biomarkers, and electronic health records. Artificial intelligence and machine learning, including deep learning and natural language processing, can integrate these heterogeneous data sources to detect early deterioration, refine risk stratification, and guide preventive strategies at the individual patient level. However, many models fail to translate due to limited external validation, biased datasets, lack of interpretability, and unclear regulatory pathways. Progress in cardioneurology will depend on rigorous, clinically grounded AI that is transparent, reproducible, and deployable across settings.

This Research Topic welcomes contributions that advance clinically meaningful AI/ML in cardioneurology, from proof-of-concept methods to validated tools with patient-centered outcomes. We encourage multidisciplinary work spanning cardiology, neurology, critical care, data science, and biomedical engineering. Manuscripts may include, but are not limited to:
• AI/ML for early detection and prediction of cardioneurological complications (e.g., stroke, cognitive decline, autonomic dysfunction, hypoxic–ischemic injury);
• Risk stratification, prognosis, and clinical decision support using multimodal data (EHR, waveforms, imaging, biomarkers, wearables);
• AI-assisted imaging analysis and automated phenotyping (segmentation, lesion detection, cardiac/brain structure-function links);
• NLP approaches for EHR mining, cohort identification, and outcome adjudication;
• Model development best practices: external validation, fairness/bias, explainability, uncertainty, calibration, and reporting standards;
• Ethical, legal, and regulatory considerations for real-world implementation;
• AI-enabled discovery of therapeutic targets and preventive interventions relevant to cardioneurological complications.

We welcome Original Research, Brief Reports, Clinical Trials, Case Studies, Reviews (including Systematic Reviews), Methods papers, and Study Protocols.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Cardioneurology, Artificial Intelligence (AI), Machine Learning, Risk Stratification, Predictive Models, Biomarkers, Personalized Care

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.