参考资料
下面列出了相关参考的文献,一并致谢!
中文医疗大模型综述
[AI医学] 几篇关于医学领域大模型的论文或项目
https://zhuanlan.zhihu.com/p/629591953
[AI医学] 用于大模型微调训练的医疗数据集
https://zhuanlan.zhihu.com/p/630730151
llm-medical-data: 用于大模型微调训练的医疗数据集
https://github.com/donote/llm-medical-data
垂直领域大模型的一些思考及开源模型汇总
https://zhuanlan.zhihu.com/p/642611747
中国国内医学相关的类GPT语言模型(2023.8个人搜集更新)
https://zhuanlan.zhihu.com/p/649688991
也看垂直领域大模型微调落地-以医疗领域为例
https://mp.weixin.qq.com/s/5q6If6hhMGGWD7mZeRfNLg
医疗行业的8个大模型,你了解吗?
中文通用及领域大模型
https://github.com/HqWu-HITCS/Awesome-Chinese-LLM
Medical NLP Competition, dataset, large models, paper 医疗NLP领域 比赛,数据集,大模型,论文,工具包
https://github.com/FreedomIntelligence/Medical_NLP
其它相关论文及英文模型
Large language models encode clinical knowledge (Nature 202307)
https://www.nature.com/articles/s41586-023-06291-2
Large language models in medicine (Nature 202307)
https://www.nature.com/articles/s41591-023-02448-8
Google’s Med-PaLM 2
https://sites.research.google/med-palm/
(When tested on the United States Medical Licensing Examination (USMLE), the model scored 85.4 per cent, in line with the knowledge of a medical expert, it’s the first model to pass at the level of an expert..)
Promoting large-scale pre-trained foundation models and adaptation in healthcare
MedCLIP: Contrastive Learning from Unpaired Medical Images and Texts.
https://github.com/RyanWangZf/MedCLIP
https://arxiv.org/pdf/2210.10163.pdf
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
https://huggingface.co/docs/transformers/main/model_doc/blip-2
https://arxiv.org/abs/2301.12597
OpenGPT:A framework for creating grounded instruction based datasets and training conversational domain expert Large Language Models (LLMs)
https://github.com/CogStack/opengpt
https://aiforhealthcare.substack.com/p/a-large-language-model-for-healthcare
ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
https://github.com/Kent0n-Li/ChatDoctor
https://arxiv.org/pdf/2303.14070.pdf
AD-AutoGPT: An Autonomous GPT for Alzheimer’s Disease Infodemiology