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Beyond transparency: why Traditional Chinese Medicine (TCM) need explainable artificial intelligence (XAI).

Wanting Zheng¹, Yuanyuan Tong², Jinjian Huang³

¹Institute of Science,Technology and Humanities, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Chinese Medicine

|July 15, 2026

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Integrating artificial intelligence (AI) with Traditional Chinese Medicine (TCM) requires explainable AI (XAI) to bridge the epistemological gap. XAI acts as an interface for semantic translation, ensuring trustworthy and culturally coherent AI in TCM.

Area of Science:

Integrative Medicine
Artificial Intelligence
Computational Linguistics

Background:

The synergy between Artificial Intelligence (AI) and Traditional Chinese Medicine (TCM) presents novel avenues for digital healthcare, including diagnosis and treatment recommendations.
A significant hurdle in AI-TCM integration is the epistemological divergence between data-centric AI and theory-driven TCM, necessitating advanced explainability.

Purpose of the Study:

To frame AI-TCM integration as an epistemological challenge requiring explainable AI (XAI) as an epistemic interface.
To introduce conceptual frameworks—dual-layer opacity and semantic translation—for understanding and addressing XAI in TCM.
To synthesize current XAI methodologies applicable to TCM and identify criteria for high-quality explanations.

Main Methods:

Keywords:

Dual-layer opacity Epistemology Explainable artificial intelligence Knowledge graphs Large language models Multimodal diagnosis Semantic translation Syndrome differentiation Traditional Chinese Medicine

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Critical synthesis of major methodological pathways for TCM-XAI, including feature attribution, visual explanations, and large language model-based infrastructures.

Application of the dual-layer opacity and semantic translation frameworks to analyze AI-TCM integration.

Identification of core criteria for evaluating TCM-XAI: faithfulness, clinical relevance, theoretical coherence, and cultural integrity.

Main Results:

XAI is crucial for semantic translation between AI patterns and TCM clinical reasoning, moving beyond mere transparency.
Major XAI methods for TCM include feature attribution, interpretable models, knowledge-guided reasoning, and LLM-based infrastructures like chain-of-thought.
Key challenges encompass annotation uncertainty, explanation validation, privacy, fairness, and the risk of cultural misinterpretation.

Conclusions:

AI-TCM integration necessitates understanding XAI as an epistemological bridge, not just a technical tool.
Future research should focus on causal inference, neuro-symbolic reasoning, and clinician-centered evaluation for trustworthy TCM-AI systems.
Establishing robust regulatory standards is essential for the responsible development of AI in Traditional Chinese Medicine.