Yujuan Song

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About

Yujuan Song has made significant contributions at the intersection of artificial intelligence and Traditional Chinese Medicine (TCM), with a primary focus on AI-driven syndrome differentiation. Her most cited work, a 2021 review on AI solutions in TCM syndrome differentiation (18 citations), traces the evolution from the first computer diagnostic program in 1979 to modern machine learning applications. This research systematically categorizes how neural networks, expert systems, and deep learning models are being adapted to decode the complex pattern-based logic of TCM diagnostics—a challenge that bridges ancient medical wisdom with computational innovation. Song’s work highlights the critical gap between AI’s 30-year head start and its relatively late adoption in TCM, while demonstrating how modern algorithms can now achieve more nuanced pattern recognition than earlier rule-based systems. Her contributions are particularly valuable for researchers developing clinical decision support tools that respect TCM’s holistic framework while leveraging data-driven precision. By mapping the landscape of AI-TCM integration, Song provides a foundational roadmap for future work in computational ethnomedicine, where her review serves as a key reference for those seeking to modernize traditional diagnostic practices without losing their philosophical essence.