Authors - Abhishek Sawant, Manas Bhansali, Naman Shah, Mandar Kakade Abstract - The integration of Traditional Medicine (TM) into global healthcare standards faces challenges due to the gap between clinician-entered free text and standardized terminologies like ICD-11. In India, AYUSH providers must document diagnoses using local terms while also supporting dual coding across NAMASTE, ICD-11 Traditional Medicine Module 2 (TM2), and ICD-11 Biomedicine. However, most EMRs do not provide unified support for these coding systems. This paper proposes a human-centric, AI-Assisted Terminology Microservice that standardizes diagnosis entry and automates the mapping between these terminologies. The system has a hybrid architecture. A Spring Boot orchestration layer manages the terminology graph and the EMR-facing APIs. Meanwhile, a Python-based machine learning service handles semantic matching from free-text descriptions to concept codes. It uses TF-IDF features and a Linear Support Vector Machine(SVM) classifier that is trained on a Silver Standard Dataset of approximately 3,250 synthetic clinical descriptions covering 75 common health issues,morbidities, with conservative lexical augmentation applied during training to improve robustness. A safety-critical fallback mechanism was designed, which detects predictions with confidence below θ = 0.45 and directs out-ofdistribution inputs to manual search workflows. This ensures a human-in-the-loop model and makes it safe to use in clinical environments. The microservice provides APIs that are EMR-friendly and produce dual-coded FHIR format diagnosis resources. This setup ensures safety along with scalability and interoperability so that it can be deployed in diverse healthcare environment.
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