- Syndrome Differentiation Thought in Traditional Chinese Medicine.- Overview of the evaluation task for syndrome differentiation thought in traditional Chinese medicine in CHIP2024.- Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG.- A TCM Syndrome Differentiation Thinking Method Based on Chain of Thought and Knowledge Retrieval Augmentation.- Fine-Tuning Large Language Models for Syndrome Differentiation in Traditional Chinese Medicine.- Iterative Retrieval Augmentation for Syndrome Differentiation via Large Language Models.- Lymphoma Information Extraction and Automatic Coding.- Benchmark for Lymphoma Information Extraction and Automated Coding.

- Overview of the Lymphoma Information Extraction and Automatic Coding Evaluation Task in CHIP 2024.- Automatic ICD Code Generation for Lymphoma Using Large Language Models.- Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-based Methods.- Leveraging Chain of Thought for Automated Medical Coding of Lymphoma Cases.- Harnessing Retrieval-Augmented LLMs for Training-Free Tumor Coding Classification.- Hierarchical Information Extraction and Classification of Lymphoma Tumor Codes Based On LLM.- Typical Case Diagnosis Consistenc.- Benchmark of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.

- Overview of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.- The Diagnosis of Typical Medical Cases through Optimized Fine-Tuning of Large Language Models.- Utilizing Large Language Models Enhanced by Chain-of-Thought for the Diagnosis of Typical Medical Cases.- Assessing Diagnostic Consistency in Clinical Cases: A Fine-Tuned LLM Voting and GPT Error Correction Framework.- Typical Medical Case Diagnosis with Voting and Answer Discrimination using Fine-tuned LLM.- Reliable Typical Case Diagnosis via Optimized Retrieval-Augmented Generation Techniques.