AI as a catalyst for addressing the subjectivity–standardization paradox in traditional Chinese medicine diagnosis

This is the authors' abstract. We don't add key points for this paper.

Traditional Chinese Medicine (TCM) diagnosis faces a fundamental challenge in modernization: balancing individualized clinical reasoning with the need for reproducible and transparent evaluation. This tension reflects the subjectivity–standardization paradox arising from the gap between experience-based interpretation and objective assessment. In this perspective article, we propose a dual-path framework in which artificial intelligence (AI) bridges clinical observation and diagnostic reasoning. Objective phenotypic quantification transforms manifestations from the Four Diagnostic Methods into standardized representations, while computational knowledge formalization enables theory-guided reasoning beyond data-driven prediction. Through knowledge–data fusion, these pathways provide a conceptual foundation for clinically meaningful AI-assisted TCM diagnosis. However, successful clinical translation requires more than technological advancement. Future development depends on reliable evidence, interpretable reasoning, and rigorous validation. A transition from accuracy-centered algorithms toward trustworthiness-centered clinical intelligence is essential. AI should complement rather than replace clinical expertise, enhancing reproducibility, transparency, and communication of TCM diagnostic knowledge.