Applications of artificial intelligence and digital transformation in traditional medicine research and practice

Abstract

Objective: To synthesize and analyze the applications of digital transformation and artificial intelligence in traditional medicine research and practice, while identifying the foundational requirements and challenges associated with implementation. Methods: A narrative review of scientific literature on the applications of artificial intelligence and digital transformation in traditional medicine. Results: Digital transformation in traditional medicine focuses on data digitization, standardization of diagnosis and treatment, database development, and the development of decision-support tools. In research, artificial intelligence and approaches such as data mining, network pharmacology, machine learning, and multi-omics analysis can support medicinal material identification, quality control, target prediction, analysis of herbal formula mechanisms, and the identification of unique characteristics or interactions within herbal combinations. Examples include research on Qi-invigorating herbs, analysis of the synergistic mechanisms of the Panax ginseng–Astragalus membranaceus combination, and investigation of Panax ginseng in type 2 diabetes mellitus. In clinical practice, automated tongue diagnosis devices, standardized image databases, reference datasets, and electronic health records provide a foundation for AI-assisted diagnosis and monitoring. Conclusion: AI has the potential to support the entire value chain of traditional medicine; however, its effectiveness depends on data quality and interoperability, standardization of devices and procedures, clinical evaluation, data governance, and appropriate oversight by experts. Digital transformation should be regarded as a process of establishing data infrastructure and standardized workflows before expanding AI applications.