Artificial Intelligence for Spleen-Stomach Disorders in Traditional Chinese Medicine: Integrating Knowledge Graphs with Intelligent Diagnosis and Treatment

Abstract

Spleen-Stomach disorders are prevalent clinical conditions in Traditional Chinese Medicine (TCM). The complex diagnostic and treatment model used in TCM is based on a “symptom-pattern-disease-formula” framework that heavily relies on practitioners’ experience. However, this model faces several challenges, including ambiguous knowledge representation, unstructured data, and difficulties with knowledge sharing. Recent advancements in artificial intelligence, natural language processing, and medical knowledge engineering have significantly improved research on knowledge graphs (KGs) and intelligent diagnosis and treatment systems for these disorders, making these technologies crucial for modernizing TCM. This article systematically reviews two core research pathways related to Spleen-Stomach disorders. The first pathway focuses on constructing knowledge graphs for “structured knowledge representation”. This includes ontology modeling, entity recognition, relation extraction, graph fusion, semantic reasoning, visualization services, and an ensemble model to predict treatment efficacy. The second pathway involves the development of intelligent diagnosis and treatment systems, with a focus on “clinical applications”. This pathway includes key technologies such as quantitative modeling of TCM, the four diagnostic methods (inspection, auscultation-olfaction, interrogation, and palpation), semantic analysis of classical texts, pattern differentiation algorithms, and multimodal consultation recommenders. Through the synthesis and analysis of current research, several ongoing challenges have been identified. These include inconsistent models and annotation of TCM clinical knowledge, limited semantic reasoning capabilities, insufficient integration between KGs and intelligent diagnostic models, and limited clinical adaptability of existing intelligent diagnostic systems. To address these challenges, this review suggests future research directions that include enhancing heterogeneous multisource knowledge integration techniques, deepening semantic reasoning through collaborative reasoning frameworks that incorporate large language models, and developing effective cross-disease transfer learning strategies. These directions aim to improve interpretability, reasoning accuracy, and clinical applicability of intelligent diagnosis and treatment systems for Spleen-Stomach disorders in TCM.

Keywords

Knowledge graphs / Intelligent diagnosis and treatment / Spleen-Stomach disorders / Natural language processing / Large language models / Syndrome differentiation / Traditional Chinese medicine informatics

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Funding

This project was supported by grants from the National Innovation Platform Development Program(2020021105012440)

the National Natural Science Foundation of China(82172524)

Hubei Provincial Key R&D Project of Artificial Intelligence(2021BEA161)

RIGHTS & PERMISSIONS

The Author(s), under exclusive licence to the Huazhong University of Science and Technology