AI-Ready clinical data for Korean Traditional Medicine
Keywords:
Korean traditional medicine, data standardization, clinical data, artificial intelligenceAbstract
Objective: To synthesize the core requirements for building artificial intelligence (AI)-ready clinical data in traditional medicine. Methods: A narrative review of the literature on the standardization, governance, and utilization of clinical data for AI applications. Results: AI-ready data require clear provenance, consistent quality and structure, and comprehensive metadata. In traditional medicine, standardization should begin by transforming qualitative clinical concepts into recordable and measurable variables, while harmonizing devices, units, and data collection procedures. Tongue diagnosis clearly illustrates these challenges because of inter-rater variability and differences in data acquisition conditions. Establishing reference data from healthy populations and standardized image sets may support comparison, education, and the development of AI systems. In addition, data interoperability and governance are essential for enabling data sharing and reuse. Conclusion: Building AI-ready data in traditional medicine requires comprehensive standardization, from measurement, data, and terminology to governance and clinical application.
References
1. Arora A, Alderman JE, Palmer J, et al. The value of standards for health datasets in artificial intelligence-based applications. Nat Med. 2023;29(11):2929-2938. DOI:10.1038/s41591-023-02608-w.
2. World Health Organization. Kicking off the journey of artificial intelligence in traditional medicine. Published September 13, 2024.
3. International Organization for Standardization, International Electrotechnical Commission. ISO/IEC 5259-1:2024. Artificial intelligence—Data quality for analytics and machine learning (ML)—Part 1: Overview, terminology, and examples. Geneva: International Organization for Standardization; 2024.
4. International Organization for Standardization, International Electrotechnical Commission. ISO/IEC 25059:2023. Software engineering—Systems and software Quality Requirements and Evaluation (SQuaRE)—Quality model for AI systems. Geneva: International Organization for Standardization; 2023.
5. Ranc H, Elkhyat A, Servais C, Mac-Mary S, Launay B, Humbert P. Friction coefficient and wettability of oral mucosal tissue: changes induced by a salivary layer. Colloids Surf A Physicochem Eng Asp. 2006;276(1-3):155-161. DOI:10.1016/j.colsurfa.2005.10.033.
6. Health Level Seven International. Fast Healthcare Interoperability Resources (FHIR). Health Level Seven International. Accessed September 27, 2026.
7. MLCommons. Croissant Format Specification, Version 1.1. Published January 29, 2026.
8. International Organization for Standardization, International Electrotechnical Commission. ISO/IEC 23894:2023. Information technology—Artificial intelligence—Guidance on risk management. Geneva: International Organization for Standardization; 2023.
Downloads
Publication Date
Issue
Section
License
Copyright (c) 2026 Sanghun Lee, Trần Nhật Minh (Tác giả)
This work is licensed under a Creative Commons Attribution 4.0 International License.
