LLM-Medical-Transplant-Course: A Practical Course on Large Language Models in Medical Transplantation

LLM-Medical-Transplant-Course is a practical tutorial on large language models for medical transplant data, including hands-on lab notebooks and real-world cases. It aims to help data scientists and clinical researchers master practical skills for applying LLMs in transplant medicine, filling the gap in medical AI education in high-risk specialized fields.

Large language models applied in the organ transplantation field face three major challenges:

1. Domain Knowledge Barriers: Involves highly specialized knowledge such as immunology and pharmacology, with insufficient training data for general LLMs;
2. Data Sensitivity and Scarcity: Strict patient privacy protection, scarce samples, high annotation costs, making it difficult to train dedicated models from scratch;
3. Safety and Accountability Requirements: Errors may endanger lives, requiring strict validation, interpretability, clear distinction between auxiliary suggestions and clinical decisions, and compliance with regulatory requirements.

The course includes six core modules:

1. Basic Preparation: Environment configuration, model loading, privacy protection;
2. Data Preprocessing: Structured (electronic medical records), unstructured (clinical notes), multi-modal data integration;
3. Prompt Engineering: Covers tasks like information extraction, classification, summarization; strategies include few-shot learning, chain-of-thought, role setting;
4. Fine-tuning and Adaptation: Parameter-efficient fine-tuning (LoRA/QLoRA), data augmentation, medical NLP evaluation methods;
5. RAG and Knowledge Enhancement: Knowledge base construction (guidelines, knowledge graphs), retrieval optimization, generation enhancement;
6. Deployment and Monitoring: Model serviceization, continuous monitoring, compliance auditing.

The course includes three real-world cases:

1. Transplant Waiting List Priority Assistance: RAG system integrates guidelines and patient data to provide priority adjustment suggestions;
2. Immunosuppressant Regimen Interpretation: Information extraction system automatically extracts medication regimens, adjustment history, and concentration monitoring results;
3. Patient Education Dialogue System: Answers post-operative care questions and guides patients to contact medical teams (does not provide medical advice).

LLM-Medical-Transplant-Course fills an important gap in medical AI education. It not only teaches technical skills but also emphasizes the responsible application of AI in high-risk medical environments. Through real-world cases, hands-on practice, and expert guidance, it provides valuable resources for training the next generation of medical AI talents and serves as high-quality learning material for applying LLMs in clinical scenarios.

Differentiated learning paths for learners with different backgrounds:

• Clinical Doctors: Focus on modules of prompt engineering, RAG, deployment and monitoring; goal is to evaluate AI clinical applicability; time investment: ~40 hours;
• Data Scientists: Focus on modules of data preprocessing, fine-tuning, evaluation methods; goal is to develop and optimize medical NLP models; time investment: ~60 hours;
• Researchers: Complete all modules, focus on understanding method limitations and cutting-edge directions; goal is to conduct original research; time investment: ~80 hours.