MedThink: Enhancing Diagnostic Accuracy in Small Models via Teacher-Guided Reasoning Correction
Accurate clinical diagnosis requires extensive domain knowledge and complex clinical reasoning capabilities. Although large language models (LLMs) hold great potential for clinical reasoning, their high computational and memory requirements limit their deployment in resource-constrained environments. Knowledge distillation (KD) can compress LLM capabilities into smaller models, but traditional KD merely transfers superficial answer patterns and fails to preserve the structured reasoning required for reliable diagnosis. To address this, we propose a two-stage distillation framework, MedThink, designed to cultivate robust clinical reasoning in small language models (SLMs). In the first stag...
| Authors | Xinchun Su, Chunxu Luo, Lipeng Ma et al. |
|---|---|
| Primary category | cs.CY |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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