Abstract
As an emerging comprehensive discipline, artificial intelligence (AI) has been widely applied in various fields, including traditional Chinese medicine (TCM), a treasure of the Chinese nation. Realizing the organic combination of AI and TCM can promote the inheritance and development of TCM. The paper summarizes the development and application of AI in auxiliary TCM diagnosis, analyzes the bottleneck of artificial intelligence in the field of auxiliary TCM diagnosis at present, and proposes a possible future direction of its development.
Highlights
AI is the main force of the fourth scientific and technological revolution [1], which is dedicated to embodying human intelligence through computational methods
Thanks to the rapid development of microsensors [7], computer image analysis, speech recognition technology [8], and deep learning [9, 10], the programmatic innovation of Traditional Chinese medicine (TCM) has been accelerated and a milestone progress has been made in the standardization and normalization of TCM diagnosis [11]. e purpose of this paper is to review the application and development of AI in assisting TCM diagnosis and to analyze the current development bottlenecks
AI has great potential in the development of healthcare and presents an opportunity to modernize the development of TCM diagnostics
Summary
AI is the main force of the fourth scientific and technological revolution [1], which is dedicated to embodying human intelligence through computational methods It is widely used in various fields and currently mainly possesses functions such as voice and image recognition, logical reasoning ability, and emotion recognition [1, 2]. The TCM facial diagnostic detector and tongue manifestation analysis system mentioned below both benefit from the development of machine learning and neural networks, while machine learning-based speech recognition technology plays an important role in listening examination. Yao et al [30] proposed an ontology-based artificial intelligence model for medicine side-effect prediction, and these predictions were validated with neural network structures, but the model is highly dependent on sufficient clinical data, and more in-depth exploration to improve the accuracy of the predictions is necessary in the future
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