Gen-SynDi: Leveraging Knowledge-Guided Generative AI for Dual Education of Syndrome Differentiation and Disease Diagnosis

Syndrome differentiation and disease diagnosis are central to Traditional Asian Medicine (TAM) because they guide personalized treatment. Yet, most TAM courses give students few structured opportunities to practise these paired skills. We developed Gen-SynDi, a knowledge-guided generative-AI framework that links syndrome differentiation with disease diagnosis to improve training. Using standardized patient files from the National Institute for Korean Medicine Development, we built a fatigue-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. A prompt-component role analysis confirmed that our prompt design improves response fidelity, and external experts endorsed the scenarios’ realism and educational value. Gen-SynDi therefore offers a scalable bridge between textbook knowledge and clinical practice, strengthening learners’ skills in differential diagnosis and syndrome differentiation.