Evaluation of Traditional Chinese Medicine Clinical Decision Support Systems: Current Status and Future Perspectives

Corresponding author: ZHU YAO, zhongyiyaochuanren@126.com

DOI: 10.12201/bmr.202610.00046

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  Abstract: Objective To systematically review the evaluation domains and methods of clinical decision support systems (CDSS), with particular emphasis on the current status and development trends of traditional Chinese medicine clinical decision support systems (TCMCDSS), and to clarify their key evaluation priorities, existing limitations, and TCM-specific evaluation requirements, thereby providing a reference for future TCMCDSS evaluation design, clinical validation, and standardized application.Methods A literature review and thematic synthesis were conducted by searching Chinese- and English-language databases. CDSS evaluation studies were summarized in terms of evaluation domains, research methods, and evaluation levels, and representative TCMCDSS studies were further analyzed to identify TCM-specific evaluation features and development trends. Results/Conclusion CDSS evaluation has expanded from single-model performance validation to multidimensional assessment encompassing knowledge quality, system usability, clinical decision-making, patient outcomes, implementation effectiveness, health economics, and safety, ethics, and regulatory considerations. Current TCMCDSS evaluations remain predominantly focused on the model and knowledge levels. Future evaluation should build on general CDSS evaluation frameworks while placing greater emphasis on the quality of information from the four examinations, syndrome differentiation and pathogenesis assessment, logical consistency across the “symptom–pathogenesis–therapeutic principle–formula–herb” chain, prescription safety, and clinical interpretability. Real-world and lifecycle evaluation should also be strengthened.

  Key words: Clinical decision support system; Traditional Chinese medicine clinical decision support system; Syndrome differentiation; Evaluation; Artificial intelligence

  Submit time: 19 October 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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