GitHub - AI-in-Health/MedLLMsPracticalGuide: A curated list of practical guide resources of Medical LLMs (Medical LLMs Tree, Tables, and Papers)

A Practical Guide for Medical Large Language Models

If you like our project, please give us a star ⭐ on GitHub for the latest update.

This is an actively updated list of practical guide resources for Medical Large Language Models (Medical LLMs). It's based on our survey paper:

A Survey of Large Language Models in Medicine: Progress, Application, and Challenge

Hongjian Zhou^1,*, Fenglin Liu^1,*, Boyang Gu^2,*, Xinyu Zou^3,*, Jinfa Huang^4,*, Jinge Wu⁵, Yiru Li⁶, Sam S. Chen⁷, Peilin Zhou⁸, Junling Liu⁹, Yining Hua¹⁰, Chengfeng Mao¹¹, Chenyu You¹², Xian Wu¹³, Yefeng Zheng¹³, Lei Clifton¹, Zheng Li^14,†, Jiebo Luo^4,†, David A. Clifton^1,†. (*Core Contributors, †Corresponding Authors)

¹University of Oxford, ²Imperial College London, ³University of Waterloo, ⁴University of Rochester, ⁵University College London, ⁶Western University, ⁷University of Georgia, ⁸Hong Kong University of Science and Technology (Guangzhou), ⁹Alibaba, ¹⁰Harvard T.H. Chan School of Public Health, ¹¹MIT, ¹²Yale University, ¹³Tencent, ¹⁴Amazon

📣 Update News

[2024-04-04] 🍻🍻🍻 Cheers! Our GitHub repository has achieved 500 stars. We are actively developing a new version and welcome your positive criticisms and suggestions.

[2024-03-03] We have updated our Version 4. Please check it out!

[2024-02-04] 🍻🍻🍻 Cheers, Happy Chinese New Year! We have updated our Version 3. Please check it out!

[2023-12-11] We have updated our survey Version 2. Please check it out!

[2023-11-09] We released the repository and survey Version 1.

⚡ Contributing

If you want to add your work or model to this list, please do not hesitate to email fenglin.liu@eng.ox.ac.uk and jhuang90@ur.rochester.edu or pull requests. Markdown format:

* [**Name of Conference or Journal + Year**] Paper Name. [[paper]](link) [[code]](link)

🤔 What are the Goals of the Medical LLM?

Goal 1: Surpassing Human-Level Expertise.

Goal 2: Emergent Properties of Medical LLM with the Model Size Scaling Up.

🤗 What is This Survey About?

This survey provides a comprehensive overview of the principles, applications, and challenges faced by LLMs in medicine. We address the following specific questions:

How should medical LLMs be built?
What are the measures for the downstream performance of medical LLMs?
How should medical LLMs be utilized in real-world clinical practice?
What challenges arise from the use of medical LLMs?
How should we better construct and utilize medical LLMs?

This survey aims to provide insights into the opportunities and challenges of LLMs in medicine, and serve as a practical resource for constructing effective medical LLMs.

🔥 Practical Guide for Building Pipeline
📊 Practical Guide for Medical Data
🗂️ Downstream Biomedical Tasks
- Generative Tasks
- Discriminative Tasks
✨ Practical Guide for Clinical Applications
⚔️ Practical Guide for Challenges
🚀 Practical Guide for Future Directions
👍 Acknowledgement
📑 Citation

🔥 Practical Guide for Building Pipeline

Pre-training from Scratch

[Arxiv, 2023] MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. paper
[Arxiv, 2023] OphGLM：Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue. paper
[npj Digital Medicine, 2023] GatorTronGPT：A Study of Generative Large Language Model for Medical Research and Healthcare. paper
[Bioinformatics, 2023] MedCPT：Contrastive Pre-trained Transformers with Large-scale Pubmed Search Logs for Zero-shot Biomedical Information Retrieval. paper
[Bioinformatics, 2022] BioGPT：Generative Pre-trained Transformer for Biomedical Text Generation and Mining. paper
[NeurIPS, 2022] DRAGON: Deep Bidirectional Language-Knowledge Graph Pretraining. paper code
[ACL, 2022] BioLinkBERT/LinkBERT: Pretraining Language Models with Document Links. paper code
[npj Digital Medicine, 2022] GatorTron：A Large Language Model for Electronic Health Records. paper
[HEALTH, 2021] PubMedBERT：Domain-specific Language Model Pretraining for Biomedical Natural Language Processing. paper
[Bioinformatics, 2020] BioBERT: A Pre-trained Biomedical Language Representation Model for Biomedical Text Mining. paper
[ENNLP, 2019] SciBERT：A Pretrained Language Model for Scientific Text. paper
[NAACL Workshop, 2019] ClinicalBERT：Publicly Available Clinical BERT Embeddings. paper
[BioNLP Workshop, 2019] BlueBERT：Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. paper

Fine-tuning General LLMs

[Arxiv, 2023.12] From Beginner to Expert: Modeling Medical Knowledge into General LLMs. 2023. paper
[Arxiv, 2023.11] Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks. 2023. paper code
[Arxiv, 2023.10] AlpaCare: Instruction-tuned Large Language Models for Medical Application. paper code
[Arxiv, 2023.10] Med42: A Clinical Large Language Model. code
[Arxiv, 2023.10] BianQue：Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT. paper
[Arxiv, 2023.10] Qilin-Med：Multi-stage Knowledge Injection Advanced Medical Large Language Model. paper
[Arxiv, 2023.10] Qilin-Med-VL：Towards Chinese Large Vision-Language Model for General Healthcare. paper
[Arxiv, 2023.9] CPLLM: Clinical Prediction with Large Language Models. paper
[Arxiv, 2023.8] BioMedGPT/OpenBioMed Open Multimodal Generative Pre-trained Transformer for BioMedicine. paper code
[Nature Digital Medicine, 2023.8] Large Language Models to Identify Social Determinants of Health in Electronic Health Records. paper [code]
[Arxiv, 2023.8] Zhongjing：Enhancing the Chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. paper
[Arxiv, 2023.6] ClinicalGPT：Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation. 2023. paper
[Cureus, 2023.6] ChatDoctor：A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge. paper
[NeurIPS Datasets/Benchmarks Track, 2023.6] LLaVA-Med: Training a large language-and-vision assistant for biomedicine in one day. paper
[Arxiv, 2023.6] MedPaLM 2: Towards expert-level medical question answering with large language models. paper
[Arxiv, 2023.5] Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding. paper
[Arxiv, 2023.5] HuatuoGPT: HuatuoGPT, towards Taming Language Model to Be a Doctor. paper
[Arxiv, 2023.4] Baize-healthcare: An open-source chat model with parameter-efficient tuning on self-chat data. paper
[Arxiv, 2023.4] Visual Med-Alpeca: A parameter-efficient biomedical llm with visual capabilities. github
[Arxiv, 2023.4] PMC-LLaMA: Further finetuning llama on medical papers. paper
[Arxiv, 2023.4] MedPaLM M: Towards Generalist Biomedical AI. paper code
[Arxiv, 2023.4] BenTsao/Huatuo：Tuning llama model with chinese medical knowledge. paper
[Github, 2023.4] ChatGLM-Med：ChatGLM-Med: 基于中文医学知识的ChatGLM模型微调. github
[Arxiv, 2023.4] DoctorGLM：Fine-tuning your chinese doctor is not a herculean task. paper

Prompting General LLMs

[Arxiv, 2023.11] MedPrompt: Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. paper
[Arxiv, 2023.8] Dr. Knows: Leveraging a medical knowledge graph into large language models for diagnosis prediction. paper
[Arxiv, 2023.3] DelD-GPT: Zero-shot medical text de-identification by gpt-4. paper code
[Arxiv, 2023.2/5] ChatCAD/ChatCAD+: Interactive computer-aided diagnosis on medical image using large language models. paper code
[Nature, 2022.12] MedPaLM: Large language models encode clinical knowledge. paper
[Arxiv, 2022.7/2023.12] Can large language models reason about medical questions? paper

📊 Practical Guide for Medical Data

Clinical Knowledge Bases

Drugs.com
DrugBank
NHS Health
NHS Medicine
Unified Medical Language System (UMLS)
The Human Phenotype Ontology
Center for Disease Control and Prevention
National Institute for Health and Care Excellence
World Health Organization

Pre-training Data

[npj Digital Medicine, 2023] EHRs: A Study of Generative Large Language Model for Medical Research and Healthcare. paper
[Arxiv, 2023] Guidelines: A high-quality collection of clinical practice guidelines (CPGs) for the medical training of LLMs. dataset
[Arxiv, 2023] GAP-REPLAY: Scaling Medical Pretraining for Large Language Models. paper
[npj Digital Medicine, 2022] EHRs: A large language model for electronic health records. paper
[National Library of Medicine, 2022] PubMed: National Institutes of Health. PubMed Data. database
[Arxiv, 2020] PubMed: The pile: An 800gb dataset of diverse text for language modeling. paper code
[EMNLP, 2020] MedDialog: Meddialog: Two large-scale medical dialogue datasets. paper code
[NAACL, 2018] Literature: Construction of the literature graph in semantic scholar. paper
[Scientific Data, 2016] MIMIC-III: MIMIC-III, a freely accessible critical care database. paper

Fine-tuning Data

cMeKG：Chinese Medical Knowledge Graph. 2023. github
CMD.: Chinese medical dialogue data. 2023. repo
BianQueCorpus: BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT. 2023. paper
MD-EHR: ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation. 2023. paper
VariousMedQA: Multi-scale attentive interaction networks for chinese medical question answer selection. 2018. paper
VariousMedQA: What disease does this patient have? a large-scale open domain question answering dataset from medical exams. 2021. paper
MedDialog: Meddialog: Two large-scale medical dialogue datasets. 2020. paper
ChiMed: Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model. 2023. paper
ChiMed-VL: Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare. 2023. paper
Healthcare Magic: Healthcare Magic. platform
ICliniq: ICliniq. platform
Hybrid SFT: HuatuoGPT, towards Taming Language Model to Be a Doctor. 2023. paper
PMC-15M: Large-scale domain-specific pretraining for biomedical vision-language processing. 2023. paper
MedQuAD: A question-entailment approach to question answering. 2019. paper
VariousMedQA: Visual med-alpaca: A parameter-efficient biomedical llm with visual capabilities. 2023. repo
CMtMedQA:Zhongjing: Enhancing the Chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. 2023. paper
MTB: Med-flamingo: a multimodal medical few-shot learner. 2023. paper
PMC-OA: Pmc-clip: Contrastive language-image pre-training using biomedical documents. 2023. paper
Medical Meadow: MedAlpaca--An Open-Source Collection of Medical Conversational AI Models and Training Data. 2023. paper
Literature: S2ORC: The semantic scholar open research corpus. 2019. paper
MedC-I: Pmc-llama: Further finetuning llama on medical papers. 2023. paper
ShareGPT: Sharegpt. 2023. platform
PubMed: National Institutes of Health. PubMed Data. In National Library of Medicine. 2022. database
MedQA: What disease does this patient have? a large-scale open domain question answering dataset from medical exams. 2021. paper
MultiMedQA: Towards expert-level medical question answering with large language models. 2023. paper
MultiMedBench: Towards generalist biomedical ai. 2023. paper
MedInstruct-52: Instruction-tuned Large Language Models for Medical Application. 2023. paper
eICU-CRD: The eicu collaborative research database, a freely available multi-center database for critical care research. 2018. paper
MIMIC-IV: MIMIC-IV, a freely accessible electronic health record dataset. 2023. paper database
PMC-Patients: 167k open patient summaries. 2023. paper database

🗂️ Downstream Biomedical Tasks

Generative Tasks

Text Summarization

PubMed: National Institutes of Health. PubMed Data. In National Library of Medicine. database
PMC: National Institutes of Health. PubMed Central Data. In National Library of Medicine. database
CORD-19: Cord-19: The covid-19 open research dataset 2020. paper
MentSum: Mentsum: A resource for exploring summarization of mental health online posts 2022. paper
MeQSum: On the summarization of consumer health questions 2019. paper
MedQSum: Enhancing Large Language Models’ Utility for Medical Question-Answering: A Patient Health Question Summarization Approach. [paper] [code]

Text Simplification

MultiCochrane: Multilingual Simplification of Medical Texts 2023. paper
AutoMeTS: AutoMeTS: the autocomplete for medical text simplification 2020. paper

Question Answering

BioASQ-QA: BioASQ-QA: A manually curated corpus for Biomedical Question Answering 2023. paper
emrQA: emrqa: A large corpus for question answering on electronic medical records 2018. paper
CliCR: CliCR: a dataset of clinical case reports for machine reading comprehension 2018. paper
PubMedQA: Pubmedqa: A dataset for biomedical research question answering 2019. paper
COVID-QA: COVID-QA: A question answering dataset for COVID-19 2020. paper
MASH-QA: Question answering with long multiple-span answers 2020. paper
Health-QA: A hierarchical attention retrieval model for healthcare question answering 2019. paper
MedQA: What disease does this patient have? a large-scale open domain question answering dataset from medical exams 2021. paper
MedMCQA: Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering 2022. paper
MMLU (Clinical Knowledge): Measuring massive multitask language understanding 2020. paper
MMLU (College Medicine): Measuring massive multitask language understanding 2020. paper
MMLU (Professional Medicine): Measuring massive multitask language understanding 2020. paper

Discriminative Tasks

Entity Extraction

NCBI Disease: NCBI disease corpus: a resource for disease name recognition and concept normalization 2014. paper
JNLPBA: Introduction to the bio-entity recognition task at JNLPBA 2004. paper
GENIA: GENIA corpus--a semantically annotated corpus for bio-textmining 2003. paper
BC5CDR: BioCreative V CDR task corpus: a resource for chemical disease relation extraction 2016. paper
BC4CHEMD: The CHEMDNER corpus of chemicals and drugs and its annotation principles 2015. paper
BioRED: BioRED: a rich biomedical relation extraction dataset 2022. paper
CMeEE: Cblue: A chinese biomedical language understanding evaluation benchmark 2021. paper
NLM-Chem-BC7: NLM-Chem-BC7: manually annotated full-text resources for chemical entity annotation and indexing in biomedical articles 2022. paper
ADE: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports 2012. paper
2012 i2b2: Evaluating temporal relations in clinical text: 2012 i2b2 challenge 2013. paper
2014 i2b2/UTHealth (Track 1): Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus 2015. paper
2018 n2c2 (Track 2): 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records 2020. paper
Cadec: Cadec: A corpus of adverse drug event annotations 2015. paper
DDI: Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013) 2013. paper
PGR: A silver standard corpus of human phenotype-gene relations 2019. paper
EU-ADR: The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships 2012. paper
[BioCreative VII Challenge, 2021] Medications detection in tweets using transformer networks and multi-task learning. [paper] [code]

Relation Extraction

BC5CDR: BioCreative V CDR task corpus: a resource for chemical disease relation extraction 2016. paper
BioRED: BioRED: a rich biomedical relation extraction dataset 2022. paper
ADE: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports 2012. paper
2018 n2c2 (Track 2): 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records 2020. paper
2010 i2b2/VA: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text 2011. paper
ChemProt: Overview of the BioCreative VI chemical-protein interaction Track 2017. database
GDA: Renet: A deep learning approach for extracting gene-disease associations from literature 2019. paper
DDI: Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013) 2013. paper
GAD: The genetic association database 2004. paper
2012 i2b2: Evaluating temporal relations in clinical text: 2012 i2b2 challenge 2013. paper
PGR: A silver standard corpus of human phenotype-gene relations 2019. paper
EU-ADR: The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships 2012. paper

Text Classification

ADE: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports 2012. paper
2014 i2b2/UTHealth (Track 2): Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus 2015. paper
HoC: Automatic semantic classification of scientific literature according to the hallmarks of cancer 2016. paper
OHSUMED: OHSUMED: An interactive retrieval evaluation and new large test collection for research 1994. paper
WNUT-2020 Task 2: WNUT-2020 task 2: identification of informative COVID-19 english tweets 2020. paper
Medical Abstracts: Evaluating unsupervised text classification: zero-shot and similarity-based approaches 2022. paper
MIMIC-III: MIMIC-III, a freely accessible critical care database 2016. paper

Natural Language Inference

MedNLI: Lessons from natural language inference in the clinical domain 2018. paper
BioNLI: BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples 2022. paper

Semantic Textual Similarity

MedSTS: MedSTS: a resource for clinical semantic textual similarity 2020. paper
2019 n2c2/OHNLP: The 2019 n2c2/ohnlp track on clinical semantic textual similarity: overview 2020. paper
BIOSSES: BIOSSES: a semantic sentence similarity estimation system for the biomedical domain 2017. paper

Information Retrieval

TREC-COVID: TREC-COVID: constructing a pandemic information retrieval test collection 2021. paper
NFCorpus: A full-text learning to rank dataset for medical information retrieval 2016. paper
BioASQ (BEIR): A heterogenous benchmark for zero-shot evaluation of information retrieval models 2021. paper

✨ Practical Guide for Clinical Applications

Retrieval-augmented Generation

[Arxiv, 2023] Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models. paper
[JASN, 2023] Retrieve, Summarize, and Verify: How Will ChatGPT Affect Information Seeking from the Medical Literature? paper

Medical Diagnosis

[Arxiv, 2023] Leveraging a medical knowledge graph into large language models for diagnosis prediction. paper
[Arxiv, 2023] ChatCAD+/Chatcad: Interactive computer-aided diagnosis on medical image using large language models. paper code
[Cancer Inform, 2023] Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries. paper
[Nature Medicine, 2023] Large language models in medicine. paper
[Nature Medicine, 2022] AI in health and medicine. paper

Formatting and ICD-Coding

[JMAI, 2023] Applying large language model artificial intelligence for retina International Classification of Diseases (ICD) coding. paper
[ClinicalNLP Workshop, 2022] PLM-ICD: automatic ICD coding with pretrained language models. paper code

Clinical Report Generation

[Arxiv, 2023] Can GPT-4V (ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis. paper
[Arxiv, 2023] Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare. paper
[Arxiv, 2023] Customizing General-Purpose Foundation Models for Medical Report Generation. paper
[Arxiv, 2023] Towards generalist foundation model for radiology. paper code
[Arxiv, 2023] Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts. 2023. paper project code
[Arxiv, 2023] MAIRA-1: A specialised large multimodal model for radiology report generation. paper project
[Arxiv, 2023] Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation. paper
[Lancet Digit Health, 2023] Using ChatGPT to write patient clinic letters. paper
[Lancet Digit Health, 2023] ChatGPT: the future of discharge summaries?. paper
[Arxiv, 2023.2/5] ChatCAD/ChatCAD+: Interactive computer-aided diagnosis on medical image using large language models. paper code

Medical Education

[JMIR, 2023] Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. paper
[JMIR, 2023] The Advent of Generative Language Models in Medical Education. paper
[Korean J Med Educ., 2023] The impending impacts of large language models on medical education. paper
[Healthcare, 2023]Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration. paper

Medical Robotics

[ICARM, 2023] A Nested U-Structure for Instrument Segmentation in Robotic Surgery. paper
[Appl. Sci., 2023] The multi-trip autonomous mobile robot scheduling problem with time windows in a stochastic environment at smart hospitals. paper
[Arxiv, 2023] GRID: Scene-Graph-based Instruction-driven Robotic Task Planning. paper
[I3CE, 2023] Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis. paper
[STAR, 2016] Advanced robotics for medical rehabilitation. paper

Medical Language Translation

[New Biotechnology, 2023] Machine translation of standardised medical terminology using natural language processing: A Scoping Review. paper
[JMIR, 2023] The Advent of Generative Language Models in Medical Education. paper
[Korean J Med Educ., 2023] The impending impacts of large language models on medical education. paper

Mental Health Support

[Arxiv, 2024] Large Language Models in Mental Health Care: a Scoping Review. paper
[Arxiv, 2023] PsyChat: A Client-Centric Dialogue System for Mental Health Support. paper code
[Arxiv, 2023] Benefits and Harms of Large Language Models in Digital Mental Health. paper
[CIKM, 2023] ChatCounselor: A Large Language Models for Mental Health Support. paper code
[HCII, 2023] Tell me, what are you most afraid of? Exploring the Effects of Agent Representation on Information Disclosure in Human-Chatbot Interaction. paper
[IJSR, 2023] A Brief Wellbeing Training Session Delivered by a Humanoid Social Robot: A Pilot Randomized Controlled Trial. paper
[CHB, 2015] Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. paper

⚔️ Practical Guide for Challenges

Hallucination

[ICLR Submission, 2024] Chain-of-verification reduces hallucination in large language models. paper
[ACM Computing Surveys, 2023] Survey of hallucination in natural language generation. paper
[EMNLP, 2023] Med-halt: Medical domain hallucination test for large language models. paper
[Arxiv, 2023] A survey of hallucination in large foundation models. 2023. paper code
[EMNLP, 2023] Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. 2023. paper
[EMNLP Findings, 2021] Retrieval augmentation reduces hallucination in conversation. 2021. paper

Lack of Evaluation Benchmarks and Metrics

[ACL, 2023] HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models. paper code
[ACL, 2022] Truthfulqa: Measuring how models mimic human falsehoods. paper
[Appl. Sci, 2021] What disease does this patient have? a large-scale open domain question answering dataset from medical exams. paper

Domain Data Limitations

[ICLR Submission, 2024] Textbooks Are All You Need. paper
[Arxiv, 2023] Model Dementia: Generated Data Makes Models Forget. paper

New Knowledge Adaptation

[ACL Findings, 2023] Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark. paper
[EMNLP, 2023] Editing Large Language Models: Problems, Methods, and Opportunities. paper
[NeurIPS, 2020] Retrieval-augmented generation for knowledge-intensive nlp tasks. paper

Behavior Alignment

[JMIR Medical Education, 2023] Differentiate ChatGPT-generated and Human-written Medical Texts. paper
[Arxiv, 2023] Languages are rewards: Hindsight finetuning using human feedback. paper code
[Arxiv, 2022] Training a helpful and harmless assistant with reinforcement learning from human feedback. paper code
[Arxiv, 2022] Improving alignment of dialogue agents via targeted human judgements. paper
[ICLR, 2021] Aligning AI with shared human values. paper code
[Arxiv, 2021.12] Webgpt: Browser-assisted question-answering with human feedback. paper

Ethical, Legal, and Safety Concerns

[Arxiv, 2023.10] A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics. paper
[Arxiv, 2023.8] "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. paper code
[NeurIPS, 2023.7] Jailbroken: How does llm safety training fail?. paper
[EMNLP, 2023.4] Multi-step jailbreaking privacy attacks on chatgpt. paper
[Healthcare, 2023.3] ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. paper
[Nature News, 2023.1] ChatGPT listed as author on research papers: many scientists disapprove. paper

🚀 Practical Guide for Future Directions

Introduction of New Benchmarks

[Arxiv, 2023.12] Designing Guiding Principles for NLP for Healthcare: A Case Study of Maternal Health. paper
[JCO CCI, 2023] Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy. [paper] [code]
[JAMA ONC, 2023] Use of Artificial Intelligence Chatbots for Cancer Treatment Information. [paper] [code]
[BioRxiv, 2023] A comprehensive benchmark study on biomedical text generation and mining with ChatGPT. paper
[JAMA, 2023] Creation and adoption of large language models in medicine. paper
[Arxiv, 2023] Large Language Models in Sport Science & Medicine: Opportunities, Risks and Considerations. paper
[Arxiv, 2023] VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence. paper

Interdisciplinary Collaborations

[JAMA, 2023] Creation and adoption of large language models in medicine. 2023. paper
[JAMA Forum, 2023] ChatGPT and Physicians' Malpractice Risk. paper

Multi-modal LLM

[Arxiv, 2023] A Survey on Multimodal Large Language Models. paper
[Arxiv, 2023] Mm-react: Prompting chatgpt for multimodal reasoning and action. paper
[Int J Oral Sci, 2023] ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. paper
[MIDL, 2023] Frozen Language Model Helps ECG Zero-Shot Learning. paper
[Arxiv, 2023] Exploring and Characterizing Large Language Models For Embedded System Development and Debugging. paper

Medical Agents

[Arxiv, 2023] The Rise and Potential of Large Language Model Based Agents: A Survey. paper
[Arxiv, 2023] MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning. paper code
[Arxiv, 2023] GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information. paper code
[MedRxiv, 2023] OpenMedCalc: Augmentation of ChatGPT with Clinician-Informed Tools Improves Performance on Medical Calculation Tasks. paper
[NEJM AI, 2024] Almanac — Retrieval-Augmented Language Models for Clinical Medicine. paper

👍 Acknowledgement

LLMs Practical Guide. The codebase we built upon and it is a comprehensive LLM survey.
Large AI Survey. Large AI Models in Health Informatics: Applications, Challenges, and the Future.
Nature Medicine. A Survey of the Large language models in medicine.
Healthcare LLMs Survey. A Survey of Large Language Models for Healthcare.

📑 Citation

Please consider citing 📑 our papers if our repository is helpful to your work, thanks sincerely!

@article{zhou2023survey,
  title={A Survey of Large Language Models in Medicine: Progress, Application, and Challenge},
  author={Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo, David A. Clifton},
  journal={arXiv preprint arXiv:2311.05112},
  year={2023}
}

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