Artificial intelligence in traditional medicine: evidence, barriers, and a research roadmap for personalized care
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Issued Date
2025-01-01
Resource Type
eISSN
26248212
Scopus ID
2-s2.0-105016793997
Journal Title
Frontiers in Artificial Intelligence
Volume
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Artificial Intelligence Vol.8 (2025)
Suggested Citation
Jongjiamdee K., Pornwonglert P., Na Bangchang N., Akarasereenont P. Artificial intelligence in traditional medicine: evidence, barriers, and a research roadmap for personalized care. Frontiers in Artificial Intelligence Vol.8 (2025). doi:10.3389/frai.2025.1659338 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112305
Title
Artificial intelligence in traditional medicine: evidence, barriers, and a research roadmap for personalized care
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Traditional medicine (TM) systems such as Ayurveda, Traditional Chinese Medicine (TCM), and Thai Traditional Medicine (TTM) are increasingly intersecting with artificial intelligence (AI). Objective: To synthesize how AI is currently applied to TM and to outline barriers and research needs for safe, equitable, and scalable adoption. Methods: We conducted a targeted narrative mini review of peer reviewed studies (2017–Aug 2025) retrieved from PubMed, Scopus, and Google Scholar using terms spanning TM (Ayurveda/TCM/TTM) and AI (machine learning (ML), natural language processing (NLP), computer vision, telemedicine. Inclusion favored studies with reported methods and, when available, performance metrics; commentary and preprints without data were excluded. Findings: Current evidence supports AI assisted diagnostic pattern recognition, personalization frameworks integrating multi source data, digital preservation of TM knowledge, telemedicine enablement, and AI supported herbal pharmacology and safety assessment. Reported performance varies and is context dependent, with limited prospective external validation. Limitations: Evidence heterogeneity, small datasets, inconsistent ontologies across TM systems, and nascent regulatory pathways constrain real world deployment. Conclusion: AI can augment TM education, research, and clinical services, but progress requires standards, culturally informed datasets, prospective trials, and clear governance. We propose a research roadmap to guide rigorous and ethical integration.