Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/KaiHe-better/LLM-for-Healthcare
https://github.com/KaiHe-better/LLM-for-Healthcare
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/KaiHe-better/LLM-for-Healthcare
- Owner: KaiHe-better
- Created: 2023-06-27T11:52:34.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-31T10:22:01.000Z (5 months ago)
- Last Synced: 2024-06-01T04:23:20.826Z (5 months ago)
- Language: Python
- Size: 2.78 MB
- Stars: 96
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Medical-Healthcare-Dataset-For-LLM - LLM-for-Healthcare - A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics. (Other Awesome List / 英文)
README
# A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
- [🔔News](#news)
- [🔔Table of Contents](#table-of-contents)
- [🔔Important Tables and Figures](#important-tables-and-figures)
- [🔔LLM Information](#llm-information)
- [🔔PLM Information](#plm-information)
- [🔔Availble Training Data](#Availble-training-data)
- [🔔Citation](#citation)
## News
- **2023-05-31 update.new paper "Polaris: A Safety-focused LLM Constellation Architecture for Healthcare"**
- **2023-05-31 update.new paper "Medical mT5: an open-source multilingual text-to-text LLM for the medical domain"**
- **2023-05-31 update.new paper "Apollo: An Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People"**
- **2023-05-31 update.new paper "LLM-CXR: INSTRUCTION-FINETUNED LLM FOR CXR IMAGE UNDERSTANDING AND GENERATION"**
- **2023-05-31 update.new paper "Me LLaMA: Foundation large language models for medical applications"**
- **2023-05-31 update.new paper "BioMistral: A collection of open-source pretrained large language models for medical domains"**
- **2023-05-31 update.new paper "OncoGPT: A medical conversational model tailored with oncology domain expertise on a large language model Meta-AI (LLaMA)"**
- **2023-03-17 update.new paper "Health-LLM: Personalized Retrieval-Augmented Disease Prediction System"**
- **2023-03-17 update.new paper "HealAI: A Healthcare LLM for Effective Medical Documentation"**
- **2023-03-17 update.new paper "BiMediX: Bilingual Medical Mixture of Experts LLM"**
- **2023-03-17 update.new paper "JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability"**
- **2023-03-17 update.new paper "MedChatZH: A tuning LLM for traditional Chinese medicine consultation"**
- **2023-10-18 added new paper "Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue".**
- **2023-10-18 added new paper "Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model".**
- **2023-10-9 We release the version 1 of the survey (https://arxiv.org/abs/2310.05694).**
## Table of Contents
- Introduction
- What LLMs Can Do for Healthcare? From Fundamental Tasks to Advanced Applications
- NER and RE for Healthcare Alpacare
- Text Classification for Healthcare
- Semantic Textual Similarity for Healthcare
- Question Answering for Healthcare
- Dialogue System for Healthcare
- Generation of Medical Reports from Images
- Summary
- From LMs to LLMs for Healthcare
- LMs for Healthcare
- LLMs for Healthcare
- Train and Use LLM for Healthcare
- Pre-training Methods
- Masked Language Modeling
- Next Word Prediction
- Sequence-to-sequence MLM
- Replaced Token Detection
- Sentence Boundary Detection
- Next Sentence Prediction
- Sentence Order Prediction
- Post-training Methods
- From predicting tokens to follow instructions: Instruction Fine-Tuning and Supervised Fine-tuning
- Reinforced Learning from Human Feedback
- From Human Feedback to AI Feedback
- Summary
- Usage
- From Fine-tuning to In-context Learning
- From System 1 Deep Learning To System 2 Deep Learning: Chain-of-Thought
- AI Agents
- Summary
- Parameters-, Memory-, and Compute-efficient Methods
- Parameters-efficient Methods
- Compute-efficient and Memory-efficient Methods
- Useful Resources
- OpenBMB
- DeepSpeed Chat
- Training Data
- Summary
- Evaluation Method
- General NLP tasks Evaluation
- Healthcare Evaluation
- Evaluation of Robustness, Bias, and Ethics
- Future Directions for Health Evaluation
- Summary
- Improving Fairness, Accountability, Transparency, and Ethics
- Fairness
- Accountability
- Transparency
- Ethics
- Future work and Conclusion
- Future Work
- Medical knowledge enhancement
- Integration with Healthcare process
- Effective Interaction with Patients and Doctors
- Hallucinations, Misunderstandings and Prompt Brittleness
- Conclusion
## Important Tables and Figures
Fig. 2. The organizational framework for the content. Section III, Section IV, Section V are technology details, while Section II, Section VI and Section VI
are more valued for Healthcare professionals
![Alt text]()
---
### LLM Information
| **Model Name** | **Base** | **Para. (B)** | **Features** | **Date** | **Link** |
|--------------------------------------------|----------------|-------------------|-------------------------------------------------|----------|------------------------------------------------------------------------|
| GatorTron | Transformer | 0.345, 3.9, 8.9 | Training from scratch | 06/2022 | https://github.com/uf-hobi-informatics-lab/GatorTron |
| Codex-Med | GPT-3.5 | 175 | CoT, Zero-shot | 07/2022 | https://github.com/vlievin/medical-reasoning |
| Galactica | Transformer | 1.3, 6.4, 30, 120 | Reasoning, Multidisciplinary | 11/2022 | https://galactica.org |
| Med-PaLM | Flan-PaLM/PaLM | 540 | CoT, Self-consistency | 12/2022 | - |
| GPT-4-Med | GPT-4 | - | no specialized prompt crafting | 03/2023 | - |
| DeID-GPT | GPT-4 | - | De-identifying | 03/2023 | https://github.com/yhydhx/ChatGPT-API |
| ChatDoctor | LLaMA | 7 | Retrieve online, external knowledge | 03/2023 | https://github.com/Kent0n-Li/ChatDoctor |
| DoctorGLM | ChatGLM | 6 | Extra prompt designer | 04/2023 | https://github.com/xionghonglin/DoctorGLM |
| MedAlpaca | LLaMA | 7, 13 | Adapt to Medicine | 04/2023 | https://github.com/kbressem/medAlpaca |
| BenTsao | LLaMA | 7 | Knowledge graph | 04/2023 | https://github.com/SCIR-HI/ Huatuo-Llama-Med-Chinese |
| PMC-LLaMA | LLaMA | 7 | Adapt to Medicine | 04/2023 | https://github.com/chaoyi-wu/PMC-LLaMA |
| Visual Med-Alpaca | LLaMA | 7 | multimodal generative model, Self-Instruct | 04/2023 | https://github.com/cambridgeltl/visual-med-alpaca |
| BianQue~ | ChatGLM | 6 | Chain of Questioning | 04/2023 | https://github.com/scutcyr/BianQue |
| Med-PaLM 2 | PaLM 2 | 340 | Ensemble refinement, CoT, Self-consistency | 05/2023 | - |
| GatorTronGPT | GPT-3 | 5, 20 | Training from scratch for medicine | 05/2023 | https://github.com/uf-hobi-informatics-lab/GatorTronGPT |
| HuatuoGPT | Bloomz | 7 | Reinforced learning from AI feedback | 05/2023 | https://github.com/FreedomIntelligence/HuatuoGPT |
| ClinicalGPT | BLOOM | 7 | multi-round dialogue consultations | 06/2023 | - |
| MedAGI | MiniGPT-4 | - | multimodal, AGI | 06/2023 | https://github.com/JoshuaChou2018/MedAGI |
| LLaVA-Med | LLaVA | 13 | multimodal, self-instruct, curriculum learning | 06/2023 | https://github.com/microsoft/LLaVA-Med |
| OphGLM | ChatGLM | 6 | multimodal, Ophthalmology LLM | 06/2023 | https://github.com/ML-AILab/OphGLM |
| SoulChat | ChatGLM | 6 | Mental Healthcare | 06/2023 | https://github.com/scutcyr/SoulChat |
| Med-Flamingo | Flamingo | 80B | multimodal, Few-Shot generative medical VQA | 07/2023 | https://github.com/snap-stanford/med-flamingo |
---
### PLM Information
TABLE I
BRIEF SUMMARIZATION OF EXISTING PLMS FOR HEALTHCARE.
| Model Name | Base | Para. (B) | Features | Date | Link |
|----------------------------------------------------|-------------|-----------|---------------------------------------|---------|----------------------------------------------------------------------------------------------------------|
| BioBERT | BERT | 0.34 | Biomedical Adaption | 05/2019 | https://github.com/naver/biobert-pretrained |
| BlueBERT | BERT | 0.34 | Biomedical Benchmark | 06/2019 | https://github.com/ncbi-nlp/BLUE\_Benchmark |
| MIMIC-BERT | BERT | 0.34 | Clinical Concept Extraction | 08/2019 | - |
| BioFLAIR~ | BERT | 0.34 | Less Computationally Intensive | 08/2019 | https://github.com/zalandoresearch/flair |
| Bio-ELECTRA-small | ELECTRA | 0.03 | Training From Scratch | 03/2020 | - |
| AlphaBERT | BERT | 0.11 | Character-level | 04/2020 | https://github.com/wicebing/AlphaBERT.git |
| Spanish-bert | BERT | - | Spanish | 04/2020 | - |
| GreenCovidSQuADBERT | BERT | 0.34 | CPU-only, CORD-19 | 04/2020 | https://github.com/npoe/covid-qa |
| BEHRT | Transformer | - | Training From Scratch | 04/2020 | https://github.com/deepmedicine/BEHRT |
| BioMed-RoBERTa | RoBERTa | 0.11 | Biomedical Adaption | 05/2020 | https://github.com/allenai/dont-stop-pretraining |
| RadBERT~ | BERT | - | RadCore Radiology Reports | 05/2020 | - |
| CT-BERT~ | BERT | 0.34 | COVID-19 | 05/2020 | https://github.com/digitalepidemiologylab/covid-twitter-bert |
| French-BERT | BERT | 0.11 | French Language Models | 06/2020 | - |
| FS-/RAD-/GER-BERT | BERT | 0.11 | Chest Radiograph Reports | 07/2020 | https://github.com/fast-raidiology/bertfor-radiology |
| Japanese-BERT | BERT | 10.11 | Japanese Clinical Narrative | 07/2020 | ai-health.m.u-tokyo.ac.jp/home/research/uth-bert |
| MC-BERT | BERT | 0.11 | Chinese Biomedical Benchmark | 08/2020 | https://github.com/alibabaresearch/ChineseBLUE |
| BioALBERT-ner | ALBERT | 0.18 | Biomedical NER | 09/2020 | https://github.com/usmaann/BioALBERT |
| BioMegatron | Megatron | 1.2 | Training From Scratch | 10/2020 | https://github.com/NVIDIA/NeMo |
| CharacterBERT | BERT | 0.11 | Character-CNN module | 10/2020 | https://github.com/helboukkouri/character-bert |
| ClinicalBert | BERT | 0.11 | For Predicting Hospital Readmission | 11/2020 | https://github.com/kexinhuang12345/clinicalBERT |
| Clinical XLNet | XLNet | 0.11 | Temporal Information | 11/2020 | https://github.com/lindvalllab/clinicalXLNet |
| Bio-LM | RoBERTa | 0.34 | Biomedical Adaption | 11/2020 | https://github.com/facebookresearch/bio-lm |
| BioBERTpt | BERT | 0.11 | Portuguese Clinical | 11/2020 | https://github.com/HAILab-PUCPR/BioBERTpt |
| RoBERTa-MIMIC | RoBERTa | 0.11 | Clinical Concept Extraction | 12/2020 | https://github.com/uf-hobi-informatics-lab/ClinicalTransformerNER |
| Clinical KB-ALBERT | ALBERT | 0.03 | Introducing Medical KB | 12/2020 | https://github.com/noc-lab/clinical-kb-bert |
| CHMBERT | BERT | 0.11 | Chinese Medical, Cloud Computing | 01/2021 | - |
| PubMedBERT | BERT | 0.11 | Training From Scratch | 01/2021 | https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
| ouBioBERT | BERT | 0.11 | Up-sampling, Amplified Vocabulary | 02/2021 | https://github.com/sy-wada/blue\_benchmark\_with\_transformers |
| BERT-EHR | BERT | - | Depression,Chronic Disease Prediction | 03/2021 | https://github.com/lanyexiaosa/brltm |
| AraBERT | BERT | 0.11 | Arabic Language | 03/2021 | https://github.com/aub-mind/araBERT |
| ABioNER | BERT | 0.11 | Arabic NER | 03/2021 | - |
| ELECTRAMed | ELECTRA | 0.11 | Biomedical Adaption | 04/2021 | https://github.com/gmpoli/electramed |
| KeBioLM | PubMedBERT | 0.11 | Introducing Medical KB | 04/2021 | https://github.com/GanjinZero/KeBioLM |
| SINA-BERT | BERT | 0.11 | Persian Language | 04/2021 | - |
| Med-BERT | BERT | 0.11 | Stay Length Prediction | 05/2021 | https://github.com/ZhiGroup/MedBERT |
| Galén | RoBERTa | 0.11 | Spanish Language | 05/2021 | https://github.com/guilopgar/ClinicalCodingTransformerES |
| SCIFIVE~ | T5 | 0.77 | Biomedical Text Generation | 05/2021 | https://github.com/justinphan3110/SciFive |
| BioELECTRA | ELECTRA | 0.34 | Training From Scratch | 06/2021 | https://github.com/kamalkraj/BioELECTRA |
| UmlsBERT | BERT | 0.11 | Introducing Medical KB | 06/2021 | https://github.com/gmichalo/UmlsBERT |
| MedGPT | GPT-2 | 1.5 | Temporal Modelling | 07/2021 | - |
| MentalBERT | BERT | 0.11 | Mental Healthcare | 10/2021 | https://huggingface.co/mental |
| CODER | mBERT | 0.34 | Cross-lingual, Introducing Medical KB | 02/2022 | https://github.com/GanjinZero/CODER |
| BioLinkBERT~ | BERT | 0.34 | PubMed with Citation Links | 03/2022 | https://github.com/michiyasunaga/LinkBERT |
| BioALBERT | ALBERT | 0.03 | Biomedical Adaption | 04/2022 | https://github.com/usmaann/BioALBERT |
| BioBART~ | BART | 0.4 | Biomedical NLG | 04/2022 | https://github.com/GanjinZero/BioBART |
| SAPBERT | BERT | 0.11 | Self-Alignment Pretraining | 10/2022 | https://github.com/cambridgeltl/sapbert |
| VPP | BART | 0.14 | Soft prompt, Biomedical NER | 03/2023 | https://github.com/KaiHe-better/VPP |
| KAD | BERT | - | Multimodal, Chest Radiology Images | 03/2023 | https://github.com/xiaoman-zhang/KAD |
---
TABLE II
SUMMARIZATION OF TRAINING DATA AND EVALUATION TASKS FOR
EXISTING PLMS FOR HEALTHCARE.
| Model Name | Method | Training Data | Eval task |
|----------------------------------------------------|--------|--------------------------------|-------------------------------------------------------|
| BioBERT | FT | PubMed, PMC | Biomedical NER, RE, QA |
| BlueBert | FT | PubMed, MIMIC-III | BLUE |
| MIMIC-BERT | FT | MIMIC-III | Biomedical NER |
| BioFLAIR~ | FT | PubMed | Bio NER |
| Bio-ELECTRA-small | PT | PubMed | Biomedical NER |
| AlphaBERT | FT | Discharge diagnoses | Extractive Summarization Task |
| Spanish-bert | FT | Spanish | Spanish Clinical Case Corpus |
| GreenCovidSQuADBERT | FT | CORD19, PubMed, PMC | NER, QA |
| BEHRT | PT | CPRD, HES | Disease Prediction |
| BioMed-RoBERTa | FT | BIOMED | CHEMPROT, RCT |
| RadBERT~ | FT | Radiology Report Corpus | Report Coding, Summarization |
| CT-BERT~ | FT | Tweet | COVID-19 Text Classification |
| French-BERT | FT | French clinical documents | DEFT challenge |
| FS-/RAD-/GER-BERT | FT,PT | Unstructured radiology reports | Chest Radiograph Reports Classification |
| Japanese-BERT | FT | Japanese EHR | Symptoms Classification |
| MC-BERT | FT | Chinese EHR | Chinese Biomedical Evaluation benchmark |
| BioALBERT-ner | FT | PubMed, PMC | Biomedical NER |
| BioMegatron | PT | PubMed | biomedical NER, RE, QA |
| CharacterBERT | Bert | OpenWebText, MIMIC-III, PMC | Medical NER, NLI, RE, SS |
| ClinicalBert | FT | MIMIC-III | Hospital Readmission Prediction |
| Clinical XLNet | FT | MIMIC-III | PMV, Mortality |
| Bio-LM | FT | PubMed, PMC, MIMIC-III | 18 Biomedical NLP Tasks |
| BioBERTpt | FT | Private clinical notes, WMT16 | SemClinBr |
| RoBERTa-MIMIC | FT | i2b2 2010, 2012, n2c2 2018 | i2b2 2010, 2012, N2C2 2018 |
| Clinical KB-ALBERT | FT | MIMIC-III, UMLS | MedNLI, i2b2 2010, 2012 |
| CHMBERT | FT | Medical text data | Disease Prediction |
| PubMedBERT | PT | PubMed | BLURB |
| ouBioBERT | FT | PubMed, Wikipedia | BLUE |
| BERT-EHR | FT | General EHR | Myocardial Infarction, Breast Cancer, Liver Cirrhosis |
| AraBERT | PT | Arabic Wikipedia, OSIAN | Arabic SA, NER, QA |
| ABioNER | FT | Arabic scientific literature | Arabic NER |
| ELECTRAMed | FT | PubMed | Biomedical NER, RE, and QA |
| KeBioLM | FT | PubMed | BLURB |
| SINA-BERT | FT | Online Persian source | Persian QA, SA |
| Med-BERT | FT | General EHR | Disease prediction |
| Galén | FT | Private clinical cases | CodiEsp-D, CodiEsp-P, Cantemist-Coding tasks |
| SCIFIVE~ | T5 | PubMed, PMC | Biomedical NER, RE, NIL, QA |
| BioELECTRA | PT | PubMed, PMC | BLURB, BLUE |
| UmlsBERT | FT | MIMIC-III | MedNLI, i2b2 2006,2010, 2012, 2014 |
| MedGPT | FT | MIMIC-III, private EHRs | Disorder Prediction |
| MentalBERT | FT | Reddit | Depression Stress, Suicide Detection, |
| CODER | FT | UMLS | MCSM, Medical RE |
| BioLinkBERT~ | FT | PubMed | BLURB, USMLE |
| BioALBERT | FT | PubMed, PMC, MIMIC-III | 6 BioNLP Tasks |
| BioBART~ | FT | PubMed | Biomedical EL, NER, QA, Dialogue, Summarization |
| SAPBERT | FT | UMLS | MEL |
| VPP | FT | PubMed | Biomedical NER |
| KAD | FT | MIMIC-CXR | PadChest, ChestXray14, CheXpert and ChestX-Det10 |
---
### Availble Training Data
| **Data** | **Type** | **size** | **Link** |
|-----------------------------------------------------|---------------------------------------------------|-------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|
| MIMIC-III | EHR | 58,976 hospital admissions for 38,597 patients | https://mimic.mit.edu/docs/iii/ |
| MIMIC-IV | EHR | covering a decade of admissions between 2008 and 2019 | https://mimic.mit.edu/docs/iv/ |
| CPRD | EHR | over 2,000 primary care practices and include 60 million patients | https://cprd.com/data |
| PubMed | Scientific Literature | 35M citations and abstracts of biomedical literature | https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/ |
| PMC | Scientific Literature | 8 million full-text article records | https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk |
| RCT | Scientific Literature | 4,528 abstract | https://github.com/bwallace/RCT-summarization-data |
| MS$\hat{~}$2 | Scientific Literature | 470,402 abstract | https://github.com/allenai/ms2/ |
| CDSR | Scientific Literature | 7,805 abstract | https://github.com/qiuweipku/Plain\_language\_summarization |
| SumPubMed | Scientific Literature | 33,772 abstract | https://github.com/vgupta123/sumpubmed |
| The Pile | Scientific Literature | 825 GB English text | https://pile.eleuther.ai/ |
| S2ORC | Scientific Literature | 63,709 abstract | https://github.com/jbshp/GenCompareSum |
| CORD-19 | Scientific Literature | 1M papers | https://github.com/allenai/cord19 |
| MeQSum | Medical Question Summarization | 1000 instances | https://github.com/abachaa/MeQSum |
| CHQ-Sum | Medical Question Summarization | 1507 instances | https://github.com/shwetanlp/Yahoo-CHQ-Summ |
| UMLS | Knowledge Base | 2M entities for 900K concepts | https://www.nlm.nih.gov/research/umls/index.html |
| COMETA | Web Data (social media) | 800K Reddit posts | https://github.com/cambridgeltl/cometa |
| MedDialog | Dialogue | 3.66 million conversations | https://github.com/UCSD-AI4H/COVID-Dialogue |
| CovidDialog | Dialogue | 603 consultations | https://github.com/UCSD-AI4H/COVID-Dialogue |
| Medical Flashcards | Dialogue | 33955 instances | https://github.com/kbressem/medalpaca |
| Wikidoc | Dialogue | 67704 instances | https://huggingface.co/datasets/medalpaca/medical\_meadow\_wikidoc |
| Wikidoc Patient Information | Dialogue | 5942 instances | https://huggingface.co/datasets/medalpaca/medical\_meadow\_wikidoc\_patient\_information |
| MEDIQA | Dialogue | 2208 instances | https://huggingface.co/datasets/medalpaca/medical\_meadow\_wikidoc\_patient\_information |
| CORD-19 | Dialogue | 1056660 instances | https://huggingface.co/datasets/medalpaca/medical\_meadow\_cord19 |
| MMMLU | Dialogue | 3787 instances | https://huggingface.co/datasets/medalpaca/medical\_meadow\_mmmlu |
| Pubmed Causal | Dialogue | 2446 instances | https://huggingface.co/datasets/medalpaca/medical\_meadow\_pubmed\_causal |
| ChatDoctor | Dialogue | 215000 instances | https://github.com/Kent0n-Li/ChatDoctor |
| Alpaca-EN-AN | English Instructions | 52K instructions | https://github.com/tatsu-lab/stanford\_alpaca/blob/main/alpaca\_data.json |
| Alpaca-CH-AN | Chinese Instructions | 52K instructions | https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/tree/main/data |
| ShareGPT | Conversations | 61653 long conversations | https://huggingface.co/datasets/philschmid/sharegpt-raw |
| WebText | Web Data | 40 GB of text | https://commoncrawl.org/the-data/get-started/ |
| OpenWebText | Web Data | 38 GB of text | https://skylion007.github.io/OpenWebTextCorpus/ |
| Colossal Clean Crawled Corpus | Web Data | 806 GB of text | https://www.tensorflow.org/datasets/catalog/c4 |
| OpenI | EHR, Multimodel | 3.7 million images from about 1.2 million papers | https://openi.nlm.nih.gov/faq\#collection |
| U-Xray | Multimodel | 3,955 reports and 7,470 images | https://openi.nlm.nih.gov/ |
| ROCO | Multimodel | 81,000 radiology images and corresponding captions | https://github.com/razorx89/roco-dataset |
| MedICaT | Multimodel | 17,000 images includes captions | https://github.com/allenai/medicat |
| PMC-OA | Multimodel | 1.6M image-caption pairs | https://huggingface.co/datasets/axiong/pmc\_oa\_beta |
| CheXpert | Multimodel | 224,316 chest radiographs with associated reports | https://aimi.stanford.edu/chexpert-chest-x-rays |
| PadChest | Multimodel | 160,000 images with related text | http://bimcv.cipf.es/bimcv-projects/padchest/ |
| MIMIC-CXR | Multimodel | 227,835 imaging studies for 64,588 patients | https://mimic.mit.edu/docs/iv/modules/cxr/ |
| PMC-15M | Multimodel | 15 million Figure-caption |
| pairs | https://arxiv.org/abs/2303.00915 |
| OpenPath | Multimodel | 208,414 pathology images related descriptions | https://laion.ai/blog/laion-5b/ |
### The Statistics of Computation Cost
TABLE VIII
THE STATISTICS OF COMPUTATION COST FOR EXISTING HEALTHCARE
LLM.
| **Model Name** | **Total data size** | **epoch** | **Batch size** | **GPU type** | **GPU number** | **GPU time** |
|-------------------|---------------------------------|-----------|----------------|--------------|----------------|--------------|
| Visual Med-Alpaca | 54k data points | 3 | 128 | A100-80G | 4 | 2.51 hours |
| GatorTron | \textgreater 90 billion words | 10 | - | A100 | 992 | 6 days |
| Galactica | - | - | - | A100-80G | 128 | - |
| ChatDoctor | 100k conversations | 3 | 192 | A100 | 6 | 3 hours |
| DoctorGLM | 3.5G | 1 | 4 | A100-80G | 1 | 8 hours |
| PMC-LLaMA | 75B tokens | 5 | 128 | A100 | 8 | 7 days |
| Visual Med-Alpaca | 44.8MB* (without images) | - | 128 | A100-80G | 4 | 2.51 hours |
| BianQue 1.0 | 9 million samples | 1 | - | RTX 4090 | 8 | 16 days |
| GatorTronGPT | 277B tokens | | 1,120/560 | A100-80G | 560 | 26 days |
| HuatuoGPT | 226,042 instances | 3 | 128 | A100 | 8 | - |
| LLaVA-Med | 15 million figure-caption pairs | - | - | A100 | 8 | 15 hours |
| Med-Flamingo | 1.3M image-caption pairs | - | 400 | A100-80G | 8 | 6.75 days |
---
TABLE IX
ESTIMATED FLOPS AND TRAINING TOKENS FOR DIFFERENT MODEL
SIZES.
| **Parameters** | **FLOPs** | **FLOPs (in Gopher unit)** | **Tokens** |
|----------------|-----------|------------------------------|------------------|
| 400 Million | 1.92e+19 | 1/29, 968 | 8.0 Billion |
| 1 Billion | 1.21e+20 | 1/4, 761 | 20.2 Billion |
| 10 Billion | 1.23e+22 | 1/46 | 205.1 Billion |
| 67 Billion | 5.76e+23 | 1 | 1.5 Trillion |
| 175 Billion | 3.85e+24 | 6.7 | 3.7 Trillion |
| 280 Billion | 9.90e+24 | 17.2 | 5.9 Trillion |
| 520 Billion | 3.43e+25 | 59.5 | 11.0 Trillion |
| 1 Trillion | 1.27e+26 | 221.3 | 21.2 Trillion |
| 10 Trillion | 1.30e+28 | 22515.9 | 216.2 Trillion |
## Citation
```bibtex
@misc{he2023survey,
title={A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics},
author={Kai He and Rui Mao and Qika Lin and Yucheng Ruan and Xiang Lan and Mengling Feng and Erik Cambria},
year={2023},
eprint={2310.05694},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```