The field of chronic disease management is witnessing a significant transformation due to recent advancements in big data analytics and artificial intelligence (AI). Major chronic diseases, such as cardiovascular diseases, diabetes, respiratory ailments, and cancer, remain leading causes of global mortality and morbidity, necessitating long-term healthcare solutions. Leveraging big data and sophisticated AI algorithms enables the identification of patterns and insights that were previously out of reach. This progress offers a pathway to more personalized, efficient, and effective healthcare interventions, addressing the extensive burden these diseases place on healthcare systems.
This Research Topic aims to delve into the transformative effects of big data and AI on managing major chronic diseases. The objectives are to showcase innovative research that highlights how these technologies can refine diagnostic processes, enhance treatment paradigms, improve prognostic predictions, and expedite drug discovery. The goal is to foster an interdisciplinary exchange of ideas and findings, enhancing the integration of AI and big data into everyday clinical practice and thus, elevating patient care and reducing the global chronic disease burden.
To gather further insights in the innovative applications of AI and big data within the realm of chronic disease management, we welcome articles addressing, but not limited to, the following themes: • Diagnosis: Utilizing AI for risk assessment and early detection across various data types (imaging, omics, clinical records) in chronic diseases. • Treatment Planning: Personalization of treatment strategies informed by data, including adaptive approaches and management of comorbidities. • Prognosis Prediction: Integrative multifactorial models that use behavioral, genomic, and environmental data for predicting disease progression and patient categorization. • Drug Research: Accelerating drug development, repurposing, and biomarker identification through AI for chronic diseases • Cross-cutting themes such as ethical considerations in AI deployment, harmonization of real-world data, and enhancing AI explainability in clinical settings. We welcome submissions of various manuscript types, such as original research articles, reviews, case studies, and methodological papers.
We welcome submissions of various manuscript types, including original research articles, reviews, case studies, and methodological papers. Submissions should highlight innovative approaches, challenges, and future directions in the use of big data and AI for chronic disease management.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Community Case Study
Conceptual Analysis
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
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
Community Case Study
Conceptual Analysis
Curriculum, Instruction, and Pedagogy
Data Report
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: Big Data, Artificial Intelligence, Chronic Diseases, Diagnosis, Treatment Plans, Prognosis Prediction, Drug Research
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.
