A curated list of popular Datasets, Models and Papers for LLMs in Medical/Healthcare.

Title	Institute	Date	Code
DoctorGPT	Sirajraval, GPT School	2023-08	Github
CoDoC: Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians	DeepMind, Google	2023-07	Github
Med-PaLM 2: Towards Expert-Level Medical Question Answering with Large Language Models	Google	2023-05	-
Capabilities of GPT-4 on Medical Challenge Problems	Microsoft, OpenAI	2023-03	-
BioMedLM-PubMedGPT: A purpose-built AI model trained to interpret biomedical language	Stanford CRFM, MosaicML	2022-12	HuggingFace
Med-PaLM: Large Language Models Encode Clinical Knowledge	Google	2022-12	Github
ClinicalT5: A Generative Language Model for Clinical Text	University of Oregon, Baidu	2022-12	HuggingFace
GatorTron: A large language model for electronic health records	University of Florida, NVIDIA	2022-12	HuggingFace
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining	Microsoft Research	2022-09	HuggingFace
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model	Tsinghua University	2022-04	HuggingFace
KeBioLM: Improving Biomedical Pretrained Language Models with Knowledge	Tsinghua, Alibaba	2021-04	Github
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art	Meta	2020-11	Github
BioMegatron: Larger Biomedical Domain Language Model	NVIDIA	2020-10	HuggingFace
PubMedBERT: Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing	Microsoft Research	2020-07	HuggingFace
Publicly Available Clinical BERT Embeddings	MIT CSAIL	2019-04	HuggingFace
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission	Harvard, Princeton, NYU	2019-04	Github
BioBERT: a pre-trained biomedical language representation model for biomedical text mining	Korea University	2019-01	Github

• Med-PaLM 2: Towards Expert-Level Medical Question Answering with Large Language Models [Paper]
• KeBioLM: Improving Biomedical Pretrained Language Models with Knowledge [Paper]
• BioELMo: Probing Biomedical Embeddings from Language Models [Paper]
• BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model [Paper]
• ClinicalT5: A Generative Language Model for Clinical Text [Paper]
• GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records [Paper]
• ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models [Paper] [Code]
• DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4 [Paper]
• Capabilities of GPT-4 on Medical Challenge Problems [Paper]
• BioBERT: a pre-trained biomedical language representation model for biomedical text mining [Paper]
• Publicly Available Clinical BERT Embeddings [Paper]
• BioMegatron: Larger Biomedical Domain Language Model [Paper]
• Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks [Paper]
• Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction [Paper]
• DoctorGLM: Fine-tuning your chinese doctor is not a herculean task [Paper] [Code]
• HuatuoGPT, Towards Taming Language Models To Be a Doctor [Paper] [Code]
• BioELECTRA:Pretrained Biomedical text Encoder using Discriminators [Paper]
• LinkBERT: Pretraining Language Models with Document Links [Paper]
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining [Paper]
• Large Language Models Encode Clinical Knowledge [Paper]
• A large language model for electronic health records [Paper]
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [Paper]
• BEHRT: Transformer for Electronic Health Records [Paper]
• Federated Learning of Medical Concepts Embedding using BEHRT [Paper] [Code]
• RadBERT: Adapting Transformer-based Language Models to Radiology [paper] [HuggingFace]
• Highly accurate protein structure prediction with AlphaFold [Paper] [Code]
• Accurate prediction of protein structures and interactions using a three-track neural network [Paper]
• Protein complex prediction with AlphaFold-Multimer [Paper]
• FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours [Paper] [Code]
• HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle [Paper] [Code]
• Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold [Paper] [Code]
• OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization [Paper] [Code]
• ManyFold: an efficient and flexible library for training and validating protein folding models [Paper] [Code]
• ColabFold: making protein folding accessible to all [Paper] [Code]
• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences [Paper] [Code]
• ProGen: Language Modeling for Protein Generation [Paper] [Code]
• ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing [Paper] [Code]
• Evolutionary-scale prediction of atomic level protein structure with a language model [Paper]
• High-resolution de novo structure prediction from primary sequence [Paper] [Code]
• Single-sequence protein structure prediction using a language model and deep learning [Paper]
• Improved the Protein Complex Prediction with Protein Language Models [Paper]
• MSA Transformer [Paper] [Code]
• Deciphering antibody affinity maturation with language models and weakly supervised learning [Paper]
• xTrimoABFold: De novo Antibody Structure Prediction without MSA [Paper]
• scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data [Paper] [Code]
• Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions [Paper] [Code]
• E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction [Paper] [Code]

LLM-for-Healthcare - A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics.

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@misc{medllmdata2023,
  author = {Jun Wang, Changyu Hou, Pengyong Li, Jingjing Gong ,Chen Song, Qi Shen, Guotong Xie},
  title = {Awesome Dataset for Medical LLM: A curated list of popular Datasets, Models and Papers for LLMs in Medical/Healthcare},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/onejune2018/Awesome-Medical-Healthcare-Dataset-For-LLM}},
}
​```