Evolving Diagnostic Agents in a Virtual Clinical Environment
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.
6902d7ea72739622ee92a85f
Pengcheng Qiu
claimed_verified
2025-10-30T14:18:23.497Z
63f5b314fcf95ecac2b51968
https://cdn-avatars.huggingface.co/v1/production/uploads/63f5b314fcf95ecac2b51968/XwuSCoUBMZzVVIWE2IZ34.png
pengchengqiu
Henrychur
user
Henrychur
6902d7ea72739622ee92a860
Chaoyi Wu
6902d7ea72739622ee92a861
Junwei Liu
6902d7ea72739622ee92a862
Qiaoyu Zheng
6902d7ea72739622ee92a863
Yusheng Liao
claimed_verified
2025-11-03T20:57:04.875Z
64bf44c0a0e5471066875053
/avatars/b165c0b0632e849248362b7ed65314a9.svg
Yusheng Liao
BlueZeros
user
BlueZeros
6902d7ea72739622ee92a864
Haowen Wang
6902d7ea72739622ee92a865
Yun Yue
6902d7ea72739622ee92a866
Qianrui Fan
6902d7ea72739622ee92a867
Shuai Zhen
6902d7ea72739622ee92a868
Jian Wang
6902d7ea72739622ee92a869
Jinjie Gu
6902d7ea72739622ee92a86a
Yanfeng Wang
6902d7ea72739622ee92a86b
Ya Zhang
6902d7ea72739622ee92a86c
Weidi Xie
Evolving Diagnostic Agents in a Virtual Clinical Environment
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.
A framework using reinforcement learning trains large language models as diagnostic agents, improving their performance through interactive exploration and feedback in a virtual clinical environment.
large language models LLMs reinforcement learning DiagGym diagnostics world model electronic health records DiagAgent end-to-end multi-turn diagnostic policies information yield diagnostic accuracy DiagBench physician-validated examination recommendations rubrics diagnosis process prompt-engineered agents Claude-sonnet-4 weighted rubric score