Sibai Xu

Papers

Total Citations

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About

Sibai Xu is a pioneering researcher at the intersection of artificial intelligence and Traditional Chinese Medicine (TCM), with a primary focus on AI-driven TCM syndrome differentiation. Their most cited work, a comprehensive 2021 review on AI solutions for TCM syndrome differentiation (18 citations), traces the evolution from the first computer program for TCM diagnosis in 1979 to modern machine learning applications. Xu’s major contribution lies in systematically mapping how AI methods—from early rule-based systems to contemporary deep learning—can standardize and enhance the complex, pattern-based diagnostic processes central to TCM. This work bridges a critical gap between ancient medical wisdom and cutting-edge computational techniques, offering a roadmap for integrating AI into holistic healthcare. By highlighting both historical milestones and emerging trends, Xu has provided a foundational reference for researchers exploring digital TCM, with their review serving as a key resource for those seeking to apply artificial intelligence to personalized, syndrome-based diagnosis. Their scholarship underscores the transformative potential of AI in preserving and modernizing traditional medical systems for global health applications.