Medical big data, encompassing patient records, clinical images, and genomic data among other information, represents a rich source of potential insights for improving healthcare outcomes. However, the complexity of medical big data poses significant challenges for traditional data processing methods, impeding efficient knowledge extraction. The integration of artificial intelligence (AI) into this field has been transformative, utilizing capabilities in data processing, pattern recognition, and machine learning to handle these challenges deftly.

This Research Topic aims to explore the challenges and opportunities that arise from leveraging AI in the context of medical big data to enhance disease prediction and diagnosis. It seeks to understand how AI can transform the vast amounts of healthcare data into actionable, accurate medical insights, thereby improving patient outcomes and the efficiency of healthcare systems.

To gather further insights in these transformative technologies, we welcome articles addressing, but not limited to, the following themes:
• Development and validation of AI models for early disease detection using medical big data.
• Ethical and privacy considerations in the use of medical big data for AI applications.
• Methodologies for integrating diverse data sources, such as clinical records, imaging data, and genomic information.
• Real-world implementations of AI-driven disease prediction and prevention strategies.
• Artificial intelligence algorithms used for medical diagnosis and treatment.
• Artificial intelligence for real-time health monitoring and warning of disease risks.

Contributions in the form of original research articles, review papers, case studies, and short communications are welcomed. These submissions should provide novel insights, methodological advancements, or practical applications related to the Research Topic, demonstrating the latest advancements and future directions in this dynamic area of research.

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Frontiers in Molecular Biosciences

Submit your idea

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