Interpretation of the Syndrome Differentiation Capability Threshold of Artificial Intelligence-Based Traditional Chinese Medicine Diagnostic System and the Construction of Human-Machine Collaborative Paradigm：Based on the Dimension of Cognitive Subjectivity
10.13288/j.11-2166/r.2026.20.002
- VernacularTitle:基于认知主体性的人工智能中医诊断系统辨证能力阈值阐释与人机协同路径构建
- Author:
Yingren ZHANG
1
;
Nan NAN
2
;
Wenzhi HAO
1
;
Jiaxu CHEN
1
Author Information
1. College of Traditional Chinese Medicine，Jinan University，Guangzhou，510632
2. Beijing University of Chinese Medicine
- Publication Type:Journal Article
- Keywords:
traditional Chinese medicine diagnostic system;
artificial intelligence;
cognitive subjectivity;
syndrome differentiation capability;
human-machine collaboration
- From:
Journal of Traditional Chinese Medicine
2026;67(20):2155-2160
- CountryChina
- Language:Chinese
-
Abstract:
Traditional Chinese medicine （TCM） diagnosis is a cognitive process where practitioners rely on their subjective clinical experience and clinical perception abilities to comprehensively integrate the four examinations and flexibly differentiate syndromes， embodying a simple humanistic concern for the state of life. The essential characteristics of the subjectivity and experience of TCM diagnosis determine that there is a threshold in the syndrome differentiation capability of the artificial intelligence （AI）-based TCM diagnostic systems， which refers to a critical point of capability in the process of simulating the syndrome differentiation thinking， due to its inability to replicate the subjective cognition， clinical perception， and humanistic care of the practitioners. Furthermore， this threshold encompasses both a technical threshold and a theoretical threshold. Based on the above analysis， four human-machine collaborative practice paths are proposed to balance the application of AI technology and the subjectivity of TCM syndrome differentiation， including clarifying the auxiliary diagnosis and treatment positioning of AI， optimizing algorithms to achieve technical adaptation， strengthening the cultivation of the practitioner's subjectivity， and promoting interdisciplinary cross-collaboration， aiming to provide theoretical references for the development of AI-based TCM diagnostic systems towards human-machine collaborative clinical applications.
