This repository serves as a comprehensive, curated guide to Large Language Models (LLMs) in medicine, targeting researchers, developers, and practitioners in the health tech domain. It consolidates practical resources, research papers, datasets, and benchmarks related to medical LLMs, aiming to accelerate their development and application in clinical settings.

How It Works

The project organizes information into practical guides covering LLM building pipelines (pre-training, fine-tuning, prompting), medical data resources, downstream biomedical tasks, clinical applications, and associated challenges. It leverages a curated list of academic papers, datasets, and leaderboards to provide a structured overview of the rapidly evolving field of medical LLMs.

Quick Start & Requirements

This repository is a curated list of resources and does not have a direct installation or execution command. Users are expected to follow links to papers, code repositories, and datasets as needed.

Highlighted Details

• Features a comprehensive overview of LLM applications in medicine, from building pipelines to clinical deployment.
• Includes extensive lists of medical datasets for pre-training and fine-tuning LLMs.
• Details various downstream biomedical tasks and their corresponding benchmarks and leaderboards.
• Covers practical clinical applications such as retrieval-augmented generation, medical decision-making, and clinical report generation.

Maintenance & Community

The project is actively updated, with its associated paper recently published in Nature Reviews Bioengineering. It encourages contributions via email or pull requests. Links to Twitter and YouTube are provided for community engagement.

Licensing & Compatibility

The repository itself is a collection of links and information; licensing details would depend on the individual linked projects and datasets. Compatibility for commercial use or closed-source linking is not specified and would require individual assessment of linked resources.

Limitations & Caveats

As a curated list, the repository's content is dependent on the availability and quality of external resources. The rapid pace of LLM development means some information may become outdated quickly.