Citrus is a medical language model that bridges the gap between clinical expertise and AI reasoning by emulating the cognitive processes of medical experts. The model is trained on a large corpus of simulated expert disease reasoning data in sft-stage-3, synthesized using a novel approach that accurately captures the decision-making pathways of clinicians.
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We propose a training-free reasoning approach that emulates the cognitive processes of medical experts, enabling large language models to enhance their medical capabilities in clinical diagnosis and treatment.
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In conjunction with the data construction method, we introduce a multi-stage post-training approach to further improve the model’s medical performance.
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We have made the Citrus model and its training data publicly available as open-source resources to advance research in AI-driven medical decision-making.
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We have developed and open-sourced a large-scale, updatable clinical practice evaluation dataset based on real-world data, accurately reflecting the distribution of patients in real-world settings.
