MedLLMsPracticalGuide
[Nature Reviews Bioengineering🔥] Application of Large Language Models in Medicine. A curated list of practical guide resources of Medical LLMs (Medical LLMs
What is MedLLMsPracticalGuide?
[Nature Reviews Bioengineering🔥] Application of Large Language Models in Medicine. A curated list of practical guide resources of Medical LLMs (Medical LLMs Tree, Tables, and Papers)
Overview of MedLLMsPracticalGuide
What it is
This is an actively updated curated list of practical guide resources for Medical Large Language Models (Medical LLMs). It is based on a survey paper published in Nature Reviews Bioengineering titled “Application of Large Language Models in Medicine”. The repository provides a comprehensive overview of the principles, applications, and challenges of LLMs in medicine, serving as a practical resource for constructing effective medical LLMs.
Key features
- Provides a practical guide for building pipelines: pre-training from scratch, fine-tuning general LLMs, and prompting general LLMs.
- Includes a practical guide for medical data: clinical knowledge bases, pre-training data, and fine-tuning data.
- Covers downstream biomedical tasks with a Huggingface leaderboard, generative tasks (text summarization, simplification, question answering), and discriminative tasks (entity extraction, relation extraction, text classification, etc.).
- Offers a practical guide for clinical applications: retrieval-augmented generation, medical decision-making, clinical coding, report generation, medical education, robotics, language translation, and mental health support.
- Discusses challenges such as hallucination, lack of evaluation benchmarks, domain data limitations, new knowledge adaptation, behavior alignment, and ethical/legal/safety concerns.
- Outlines future directions including new benchmarks, interdisciplinary collaborations, multi-modal LLMs, and medical agents.
Use cases
This resource is intended for researchers, clinicians, and developers who want to understand the current landscape of medical LLMs, learn how to build or fine-tune models for specific biomedical tasks, and stay updated with the latest progress in the field. It can be used as a reference for selecting appropriate data, models, and evaluation methods, as well as for identifying open challenges and future research opportunities.
Getting started
See the repository README for details on how to use the list and contribute.
License and pricing
The project is licensed under the MIT License and is open source.
Based on the project's public README and repository metadata.
