Personalizing neuro-cardiovascular care: Advances and barriers in multi-modal AI risk prediction

Cardioneurology is an interdisciplinary field that explores the intricate connections between the brain and the cardiovascular system, with the aim of improving patient outcomes through integrated approaches. Recent advances in artificial intelligence (AI), especially the development of multi-modal models, have shown promise for enhancing risk prediction by using diverse data sources such as neuroimaging, cardiac imaging, wearable sensors, and genetic profiles. However, despite remarkable progress, translating these AI-driven insights into routine clinical care remains a challenge. The current landscape is characterized by ongoing debates regarding the interpretability of models, reliability across different patient cohorts, and the safety of integrating multi-modal data for predictive purposes. Furthermore, studies highlight substantial challenges tied to algorithmic bias, data privacy, and the heterogeneity of data sources, underlining an urgent need for robust, generalizable, and transparent solutions.This Research Topic aims to foster innovative research and critical discussion around the development, evaluation, and clinical implementation of AI tools for integrative cerebrovascular and cardiovascular risk prediction. By addressing both technical and translational hurdles, this initiative encourages multidisciplinary work exploring the design of explainable models, quantifying uncertainty, and ensuring equitable, secure, and privacy-aware AI solutions. Key objectives include identifying barriers to clinical adoption, evaluating the impact of data quality and diversity, and demonstrating effective pathways for bringing multi-modal AI models to the bedside. The overarching goal is to empower the cardioneurology community to harness AI for developing truly personalized and integrative care strategies.This Research Topic welcomes original research, reviews, perspectives, methods, and case studies that focus on the intersection of precision medicine, cardioneurology, and artificial intelligence. Studies should clearly articulate the scope and limitations of their findings, emphasizing practical insights for clinical translation. To gather further insights in this space, we welcome articles addressing, but not limited to, the following themes:-Integration of multi-modal data (e.g., imaging, wearables, genetics) in AI models-Strategies for achieving model interpretability and transparency-Managing uncertainty in predictive modeling for neuro-cardiovascular risk-Addressing bias, fairness, and privacy concerns in AI applications-Translating AI-generated insights into clinical workflows and decision-makingAppendix: Article types suitable for this Research Topic include original research, reviews, perspectives, methods, and case studies.*Dr. Sudre is a scientific advisor for BrainKey Topic editors: Carole Sudre, Silvia Ingala Source: https://www.frontiersin.org/research-topics/78751/personalizing-neuro-cardiovascular-care-advances-and-barriers-in-multi-modal-ai-risk-prediction