Challenge: Existing methods for training specialized reasoning models for the medical domain are limited due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data.
Approach: They propose a framework that introduces a dedicated coach role to guide the student model through question decomposition.
Outcome: The proposed framework smooths the learning curve in medical reasoning by facilitating domain adaptation before advancing to complex long-chain reasoning.

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Challenge: Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic.
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Challenge: Recent advances in large language models have significantly influenced the field of online medical consultations, but critical challenges remain, such as the generation of hallucinated information and the integration of up-to-date medical knowledge.
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Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024.findings-acl)

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Challenge: Existing studies suggest augmenting LLMs with external text corpora to alleviate hallucination problems.
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Challenge: Existing large language models (LLMs) fail to identify information gaps across diverse symptoms.
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Challenge: Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence.
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Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs (2026.eacl-long)

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Challenge: Medical reasoning in large language models is a complex cognitive process through which clinicians interpret patient data and make diagnostic and therapeutic decisions.
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Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach (2026.findings-acl)

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Challenge: Existing methods to enhance medical reasoning lack high-quality data.
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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs (2025.coling-main)

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Challenge: GraphRAG framework is designed to enhance LLMs in generating evidence-based medical responses.
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Challenge: Recent advances in multimodal large language models have seen remarkable progress for medical decision-making, however, they are designated for specific classification or generative tasks and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing.
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