Evolving Diagnostic Agents in a Virtual Clinical Environment
Authors: name: Pengcheng Qiu, name: Chaoyi Wu, name: Junwei Liu, name: Qiaoyu Zheng, name: Yusheng Liao, name: Haowen Wang, name: Yun Yue, name: Qianrui Fan, name: Shuai Zhen, name: Jian Wang, name: Jinjie Gu, name: Yanfeng Wang, name: Ya Zhang, name: Weidi Xie
Organization: Shanghai Jiao Tong University

In this paper, we present a framework for training large language models
(LLMs) as diagnostic agents with reinforcement learning, enabling them to
manage multi-turn diagnostic processes, adaptively select examinations, and
commit to final diagnoses. Unlike instruction-tuned models trained on static
case summaries, our method acquires diagnostic strategies through interactive
exploration and outcome-based feedback. Our contributions are fourfold: (i) We
present DiagGym, a diagnostics world model trained with electronic health
records that emits examination outcomes conditioned on patient history and
recommended examination, serving as a virtual clinical environment for
realistic diagnosis training and evaluation; (ii) We train DiagAgent via
end-to-end, multi-turn reinforcement learning to learn diagnostic policies that
optimize both information yield and diagnostic accuracy; (iii) We introduce
DiagBench, a diagnostic benchmark comprising 750 cases with physician-validated
examination recommendations and 99 cases annotated with 973 physician-written
rubrics on diagnosis process; (iv) we demonstrate superior performance across
diverse diagnostic settings. DiagAgent significantly outperforms 10
state-of-the-art LLMs, including DeepSeek-v3 and GPT-4o, as well as two
prompt-engineered agents. In single-turn settings, DiagAgent achieves 9.34%
higher diagnostic accuracy and 44.03% improvement in examination recommendation
hit ratio. In end-to-end settings, it delivers 15.12% increase in diagnostic
accuracy and 23.09% boost in examination recommendation F1 score. In
rubric-based evaluation, it surpasses the next-best model, Claude-sonnet-4, by
7.1% in weighted rubric score. These findings indicate that learning policies
in interactive clinical environments confers dynamic and clinically meaningful
diagnostic management abilities unattainable through passive training alone.