ClinicalGPT-R1: A Diagnostic Reasoning LLM

• ClinicalGPT-R1 is a specialty large language model that enhances diagnostic reasoning through extended chain-of-thought and reinforcement learning techniques.
• It is trained on 20,000 clinical records and synthetic QA pairs, ensuring robust performance in diagnosing both common and rare conditions across multiple specialties.
• Benchmark results indicate superior performance in Chinese diagnostic tasks and competitive accuracy with GPT-4 in English, underscoring its potential for real-world clinical application.

ClinicalGPT-R1 is a specialty generalist LLM developed to advance diagnostic reasoning in clinical medicine through extended chain-of-thought capabilities and reinforcement learning optimization. It is trained on a heterogeneous real-world dataset encompassing authentic workflows and rare diagnoses, and evaluated on MedBench-Hard—a robust benchmark for reasoning-based disease diagnosis across multiple medical specialties. ClinicalGPT-R1 demonstrates superior performance in Chinese diagnostic tasks relative to GPT-4o, and achieves comparable accuracy to GPT-4 in English, substantiating its enhanced reasoning and multilingual applicability.

1. Training Data and Synthesis Strategies

ClinicalGPT-R1 is trained on a curated corpus of 20,000 clinical records, sourced from multi-department electronic health records (EHRs) and specialty datasets such as MedDX-FT. The records span typical case presentations and rare conditions. Rigorous classification and filtering ensure the inclusion of representative scenarios across seven clinical specialties. Synthetic data augmentation supplements real-world records to further enhance diagnostic chain reasoning. Two dedicated data synthesis models—GPT-4o-mini and Deepseek-v3-0324—generate QA triplets with explicit reasoning steps and diverse prompts, emulating authentic clinical queries, intermediate thought processes, and diagnostic conclusions.

Training proceeds in two stages:

1. Supervised Fine-Tuning (SFT): The model is trained on (question, thinking, final response) triplets to encourage explicit diagnostic reasoning before prediction.
2. Reinforcement Learning (RL): Using Proximal Policy Optimization (PPO), the RL stage refines the reasoning process by employing a result-based reward function favoring correct diagnoses and well-structured reasoning chains:

$$r'(x, \hat{y}, y^*) = \begin{cases} 1 & \text{if } \text{verifier}(\hat{y}, y^*) = \text{True} \ 0.1 & \text{if } \text{verifier}(\hat{y}, y^*) = \text{False} \ 0 & \text{if } \hat{y} = \text{null} \end{cases}$$

2. Benchmarking on MedBench-Hard

ClinicalGPT-R1 is evaluated on the MedBench-Hard benchmark, specifically designed to assess long-chain diagnostic reasoning. This dataset consists of 3,500 cases—500 per specialty across Respiratory, Gastroenterology, Urology, Cardiology, Immunology, Neurology, and Endocrinology—stratified by ICD-10 codes to ensure both common and rare disease coverage. Test cases present complex diagnostic scenarios requiring nuanced reasoning, differential diagnosis across symptom clusters, and stepwise elimination processes simulating real expert workflow.

Comparative results show ClinicalGPT-R1 outperforms GPT-4o in Chinese medical tasks, and matches GPT-4’s performance in English. The model is also superior to Qwen2.5-7B-Instruct, highlighting its reasoning capabilities across multiple language and specialty domains.

3. Reasoning Capabilities and Diagnostic Process

ClinicalGPT-R1’s chain-of-thought (CoT) reasoning is based on prompt and synthetic data that require multi-step logical deduction. Outputs include transitional and cognitive markers (“hmm,” “also,” “wait”) that distinguish explicit diagnostic processes from direct pattern-matching. Reinforcement learning is employed to penalize shortcut or jump-to-answer responses, instead encouraging the model to enumerate plausible diagnoses, intermediate findings, and diagnostic eliminations consistent with clinical guidelines.

This approach yields higher performance in specialty cases requiring iterative exclusion (for example, distinguishing autoimmune from infectious causes in immunology), or resolving ambiguous findings in neurology and endocrinology by integrating intermediate test results and symptom history.

4. Model Architecture and Technical Specifications

ClinicalGPT-R1 builds upon the Qwen2.5-7B-Instruct architecture. Training is performed in two distinct phases:

• SFT Stage: 3 epochs, cosine decay learning rate schedule (starting at $5\times 10^{-6}$).
• RL Stage: Learning rate $5\times 10^{-7}$, batch size 16, $\beta = 0.03$; PPO with 3 epochs, discount factor $\gamma = 1.0$, value coefficient 1.0, and clip range 0.2.

Reward modeling is defined as above; PPO optimizes the policy to maximize long-chain correct reasoning and final outcomes. This results in consistently transparent, stepwise predictions and diagnostic chains.

5. Comparative Performance Analysis

The model’s diagnostic reasoning and accuracy are systematically compared with GPT-4/GPT-4o (in Chinese and English) and Qwen2.5-7B-Instruct. In Chinese, ClinicalGPT-R1 substantially surpasses GPT-4o, particularly in specialty reasoning and rare disease identification. In English, its accuracy meets or exceeds GPT-4’s performance on complex clinical benchmark tasks. The superior results are attributed to the enhanced multi-step reasoning induced by synthetic data, explicit chain-of-thought supervision, and reward-modulated prediction optimization. This positions ClinicalGPT-R1 at the forefront of reasoning-augmented clinical LLMs.

6. Implications for Clinical Practice and Future Work

ClinicalGPT-R1’s transparent, extended reasoning chains facilitate greater trust and interpretability in automated clinical decision support. Its capacity for explicit, stepwise diagnostic output aligns with clinical audit needs and improves workflow integration. The model’s robust multilingual performance enables deployment across global healthcare environments.

Future directions identified include:

• Expansion of training data with broader specialty coverage and additional synthetic strategies.
• Integration with hospital information systems for real-time diagnostic support.
• Continuous model updating via feedback loops from new clinical cases.
• Enhancement of RL reward functions to better align with evolving clinical guidelines.
• Cross-domain generalization to treatment planning, prognosis, and patient education.

7. Resources and Accessibility

ClinicalGPT-R1, its benchmarks (MedBench-Hard), and associated code/resources are available at https://github.com/medfound/medfound, supporting reproducibility and open evaluation for the broader clinical NLP research community.

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ClinicalGPT-R1 establishes a new standard for diagnostic reasoning in LLMs by uniting extensive real-world clinical data with advanced reinforcement learning and chain-of-thought supervision. Its validated performance on representative benchmarks marks a substantive advancement toward reliable, interpretable, and scalable clinical decision support in multilingual healthcare environments (Lan et al., 13 Apr 2025).