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ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language model

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arxiv 2504.09421 v2 pith:EE27K47O submitted 2025-04-13 cs.CL cs.AI

classification cs.CLcs.AI
keywords clinicalgpt-r1reasoningdiagnosisdiseaselanguagelargeperformanceclinical
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advances in reasoning with large language models (LLMs)has shown remarkable reasoning capabilities in domains such as mathematics and coding, yet their application to clinical diagnosis remains underexplored. Here, we introduce ClinicalGPT-R1, a reasoning enhanced generalist large language model for disease diagnosis. Trained on a dataset of 20,000 real-world clinical records, ClinicalGPT-R1 leverages diverse training strategies to enhance diagnostic reasoning. To benchmark performance, we curated MedBench-Hard, a challenging dataset spanning seven major medical specialties and representative diseases. Experimental results demonstrate that ClinicalGPT-R1 outperforms GPT-4o in Chinese diagnostic tasks and achieves comparable performance to GPT-4 in English settings. This comparative study effectively validates the superior performance of ClinicalGPT-R1 in disease diagnosis tasks. Resources are available at https://github.com/medfound/medfound.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

    cs.AI 2026-07 accept novelty 6.0 of 10

    A dual clinical-computational taxonomy for medical LLM reasoning plus a five-level 5k-sample benchmark showing specialists excel at diagnosis and general models at decision support/dialogue.

  2. Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

    cs.AI 2026-06 unverdicted novelty 5.0 of 10

    Introduces the first structured pulmonary knowledge graph LungKG and uses it to train Lung-R1, which reaches SOTA on EMR-based pulmonary diagnosis tasks.

  3. Medical Reasoning with Large Language Models: A Survey and MR-Bench

    cs.CL 2026-03 accept novelty 5.0 of 10

    LLMs show strong exam performance on medical tasks but exhibit a clear gap in accuracy on authentic clinical decision-making as measured by the new MR-Bench benchmark and unified evaluations.

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