Presentation summary

Introduction : 
Syndrome differentiation and disease diagnosis are central to Traditional Asian Medicine (TAM) because theyguide personalized treatment. Yet, most TAM courses give students few structured opportunities to practicethese paired skills.

Method :
Using standardized patient files from the National Institute for Korean Medicine Development, we built afatigue-focused dataset covering five Western-defined diseases and seven TAM syndromes. Carefully designed prompts and a large language model produced 28 virtual patient cases by joining compatible disease–syndrome pairs while preserving clinical realism. Inside an interactive web simulation, students conduct history-taking, receive free-text answers, and propose both syndrome and disease diagnoses ; immediate feedback highlights missing questions, reasoning gaps, and overall accuracy. A built-in scoring module supplies quantitative measures of inquiry coverage and diagnostic precision, plus brief explanations of overlooked clues.

Results :
We developed Gen-SynDi, a knowledge-guided generative-AI framework that links syndrome differentiationwith disease diagnosis to improve training.
A prompt-component role analysis confirmed that our prompt design improves response fidelity, and external experts endorsed the scenarios’ realism and educational value.

Conclusion :
Gen-SynDi therefore offers a scalable bridge between textbook knowledge and clinical practice, strengthening learners’ skills in differential diagnosis and syndrome differentiation.