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REVIEW ARTICLE

Multimodal data-driven intelligent diagnosis in traditional Chinese medicine: Current status and future perspectives

Jiaqi Sun1 ,  Zheng Yu2* ,  Junhong Gao1*
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1 Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing , China
2 School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan , China
AIH, 026130032 https://doi.org/10.36922/AIH026130032
Received: 29 March 2026 | Revised: 29 April 2026 | Accepted: 13 May 2026 | Published online: 15 September 2026
(This article belongs to the Special Issue Artificial Intelligence in Traditional Chinese Medicine)
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

With advances in artificial intelligence, traditional Chinese medicine diagnosis is gradually becoming more intelligent. This review departs from the conventional framework that categorizes the four diagnostic methods in isolation. Instead, it starts from five heterogeneous data modalities, namely vision, audition, olfaction, textual, and tactile, and systematically reconstructs the digital mapping relationships of traditional Chinese medicine diagnostic information, thereby directly aligning traditional Chinese medicine diagnostic problems with the input paradigms of artificial intelligence. This study comprehensively examines the current state of research on multimodal acquisition technologies, fusion methods, and clinical applications, and analyzes the core challenges and future directions in this field. This study aims to provide a theoretical foundation for achieving truly intelligent integration of the four diagnostic methods.

Graphical abstract
Keywords
Multimodal data
Intelligent diagnosis in traditional Chinese medicine
Integrated four diagnostic methods
Data fusion
Artificial intelligence
Funding
This work was supported by Science and Technology Development Fund Project of Beijing Traditional Chinese Medicine (grant no.: BJZYZD-2025-09).
Conflict of interest
The authors declare no conflict of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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