A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
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2023-05-31 update.new paper "Polaris: A Safety-focused LLM Constellation Architecture for Healthcare"
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2023-05-31 update.new paper "Medical mT5: an open-source multilingual text-to-text LLM for the medical domain"
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2023-05-31 update.new paper "Apollo: An Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People"
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2023-05-31 update.new paper "LLM-CXR: INSTRUCTION-FINETUNED LLM FOR CXR IMAGE UNDERSTANDING AND GENERATION"
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2023-05-31 update.new paper "Me LLaMA: Foundation large language models for medical applications"
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2023-05-31 update.new paper "BioMistral: A collection of open-source pretrained large language models for medical domains"
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2023-05-31 update.new paper "OncoGPT: A medical conversational model tailored with oncology domain expertise on a large language model Meta-AI (LLaMA)"
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2023-03-17 update.new paper "Health-LLM: Personalized Retrieval-Augmented Disease Prediction System"
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2023-03-17 update.new paper "HealAI: A Healthcare LLM for Effective Medical Documentation"
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2023-03-17 update.new paper "BiMediX: Bilingual Medical Mixture of Experts LLM"
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2023-03-17 update.new paper "JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability"
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2023-03-17 update.new paper "MedChatZH: A tuning LLM for traditional Chinese medicine consultation"
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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".
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2023-10-18 added new paper "Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model".
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2023-10-9 We release the version 1 of the survey (https://arxiv.org/abs/2310.05694).
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Introduction
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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
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From LMs to LLMs for Healthcare
- LMs for Healthcare
- LLMs for Healthcare
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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
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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
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Usage
- From Fine-tuning to In-context Learning
- From System 1 Deep Learning To System 2 Deep Learning: Chain-of-Thought
- AI Agents
- Summary
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Parameters-, Memory-, and Compute-efficient Methods
- Parameters-efficient Methods
- Compute-efficient and Memory-efficient Methods
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Useful Resources
- OpenBMB
- DeepSpeed Chat
- Training Data
- Summary
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Evaluation Method
- General NLP tasks Evaluation
- Healthcare Evaluation
- Evaluation of Robustness, Bias, and Ethics
- Future Directions for Health Evaluation
- Summary
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Improving Fairness, Accountability, Transparency, and Ethics
- Fairness
- Accountability
- Transparency
- Ethics
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Future work and Conclusion
- Future Work
- Medical knowledge enhancement
- Integration with Healthcare process
- Effective Interaction with Patients and Doctors
- Hallucinations, Misunderstandings and Prompt Brittleness
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Conclusion
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
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 |
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 | 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 |
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/ |
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 |
@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}
}