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Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review

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Introduction

In the fast-evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as groundbreaking tools with the potential to emulate complex human linguistic abilities. Their profound impact on healthcare, a field at the crossroads of multifaceted data and intricate decision-making, is of immense interest. This repository delves into the integration challenges and showcases the breadth of LLMs' applications within the medical sphere.

Herein, we offer a curated anthology that navigates through the realm of general-purpose and specialized LLMs, elucidating their roles in enhancing medical research, streamlining clinical operations, and supporting diagnostic processes. We cast a spotlight on multimodal LLMs, championing their sophistication in harmonizing varied data streams such as medical imagery and electronic health records (EHRs) to refine diagnostic precision. Advancing into the frontiers of innovation, we explore LLM-empowered autonomous healthcare agents, scrutinizing their capacity for personalized care and intricate clinical reasoning. Additionally, we present a synthesis of evaluative strategies critical for verifying the dependability and security of LLMs within medical settings.

Our extensive analysis sheds light on the transformative promise LLMs hold for healthcare's future. Yet, we underscore the indispensable call for ongoing refinement and ethical vigilance as precursors to their successful clinical integration.

Please note: This repository's scope is centered on the technological evolution of LLMs in medicine. For insights into clinical deployments and applications of LLMs, we invite you to consult our comprehensive review.

We sincerely value all contributions, whether through pull requests, issue reports, emails, or other forms of communication.

Table of Content (ToC)

Evaluating General-Purpose LLMs in Medicine via Prompting

Specialized Medical LLMs

Multimodal LLMs in Medicine

GPT-4V

LLM-Powered Healthcare Agents

Evaluation

Strategies

Valuable Resources

Related Surveys

LLM Techniques

LLMs in Medicine

Repositories

Project Maintainers & Contributors

Citing

If you find this repository useful in your research, please consider citing it.

@article{yuan2023large,
  title={Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review},
  author={Yuan, Mingze and Bao, Peng and Yuan, Jiajia and Shen, Yunhao and Chen, Zifan and Xie, Yi and Zhao, Jie and Chen, Yang and Zhang, Li and Shen, Lin and others},
  journal={arXiv preprint arXiv:2311.01918},
  year={2023}
}

Licenses

MIT license This project is licensed under the terms of the MIT License.

Acknowledgement

We have structured our repository by drawing inspiration from the substantial work of repositories such as LLM-Agent-Paper-List, CareGPT, and insights from RadLLM. We extend our sincere gratitude to their contributions.