# MedAgent-RAG

Automated history-taking in telemedicine must collect a structured record over many turns while adapting to what the patient answers. MedAgent-RAG keeps an explicit clinical state of filled slots, a ranked differential diagnosis, questions already asked and red flags detected, selects every question from that state, and grounds its final assessment in clinical guidelines retrieved by a parent–child retrieval-augmented pipeline. Evaluated on the MediTOD corpus with a language-model patient simulator, it raises BERTScore F1 from 0.68 for a single-shot baseline built on the same language model to 0.84, at a cost of 15.8 turns on average.

Automated history-taking in telemedicine must collect a structured record over many turns while adapting to what the patient answers. MedAgent-RAG keeps an explicit clinical state of filled slots, a ranked differential diagnosis, questions already asked and red flags detected, selects every question from that state, and grounds its final assessment in clinical guidelines retrieved by a parent–child retrieval-augmented pipeline. Evaluated on the MediTOD corpus with a language-model patient simulator, it raises BERTScore F1 from 0.68 for a single-shot baseline built on the same language model to 0.84, at a cost of 15.8 turns on average.