Cited by 153Artificial Intelligence in Healthcare and EducationTopic ModelingMachine Learning in Healthcare

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A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

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He, Kai, Mao, Rui, Lin, Qika, Ruan, Yucheng, Lan, Xiang, Feng, Mengling, Cambria, Erik·2025·Information Fusion

DOI 10.1016/j.inffus.2025.102963

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Abstract

The utilization of large language models (LLMs) for Healthcare has generated both excitement and concern due to their ability to effectively respond to free-text queries with certain professional knowledge. This survey outlines the capabilities of the currently developed Healthcare LLMs and explicates their development process , to provide an overview of the development road map from traditional Pretrained Language Models (PLMs) to LLMs. Specifically, we first explore the potential of LLMs to enhance the efficiency and effectiveness of various Healthcare applications highlighting both the strengths and limitations. Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, and summarize related Healthcare training data , learning methods, and usage. Finally, the unique concerns associated with deploying LLMs are investigated, particularly regarding fairness, accountability, transparency, and ethics. Besides, we support researchers by compiling a collection of open-source resources 1 . Summarily, we contend that a significant paradigm shift is underway, transitioning from PLMs to LLMs. This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a move from model-centered methodologies to data-centered methodologies. We determine that the biggest obstacle of using LLMs in Healthcare are fairness, accountability, transparency and ethics.

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Med-Flamingo: a Multimodal Medical Few-shot LearnerPreprint
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Evaluating large language models on a highly-specialized topic, radiation oncology physics
Holmes, J., Liu, Z., Zhang, L., Ding, Y., Sio, T. T. W., McGee, L. A., Ashman, J. B., Li, X., Liu, T., Shen, J., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1219326 · Cited by 198
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
Lewis, P., Ott, M., Du, J., & Stoyanov, V. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.17 · Cited by 196
MedDialog: Large-scale Medical Dialogue Datasets
Zeng, G., Yang, W., Ju, Z., Yang, Y., Wang, S., Zhang, R., Zhou, M., Zeng, J., Dong, X., Zhang, R., et al. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.743 · Cited by 196
Trialling a Large Language Model (ChatGPT) in General Practice With the Applied Knowledge Test: Observational Study Demonstrating Opportunities and Limitations in Primary Care
Thirunavukarasu, A. J., Hassan, R., Mahmood, S., Sanghera, R., Barzangi, K., El Mukashfi, M., & Shah, S. (2023) · JMIR Medical Education · doi:10.2196/46599 · Cited by 187
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-World Multi-Turn Dialogue
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BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Yuan, H., Yuan, Z., Gan, R., Zhang, J., Xie, Y., & Yu, S. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.9 · Cited by 178
BioMegatron: Larger Biomedical Domain Language Model
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Thirty years of the International Banff Classification for Allograft Pathology: the past, present, and future of kidney transplant diagnostics
Loupy, A., Mengel, M., & Haas, M. (2021) · Kidney International · doi:10.1016/j.kint.2021.11.028 · Cited by 168
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
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Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023) · arXiv · doi:10.48550/arxiv.2301.12867 · Cited by 164
Improving chest X-ray report generation by leveraging warm starting
Nicolson, A., Dowling, J., & Koopman, B. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102633 · Cited by 161
Analysis of large-language model versus human performance for genetics questions
Duong, D., & Solomon, B. D. (2024) · European Journal of Human Genetics · doi:10.1038/s41431-023-01396-8 · Cited by 159
Clinical concept extraction using transformers
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MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
Pal, A., Umapathi, L. K., & Sankarasubbu, M. (2022) · arXiv · doi:10.48550/arxiv.2203.14371 · Cited by 157
Bidirectional Representation Learning From Transformers Using Multimodal Electronic Health Record Data to Predict Depression
Meng, Y., Speier, W. F., Ong, M. K., & Arnold, C. W. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3063721 · Cited by 146
Measuring algorithmically infused societies
Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021) · Nature · doi:10.1038/s41586-021-03666-1 · Cited by 144
Multiple Meta-model Quantifying for Medical Visual Question Answering
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Sources of Hallucination by Large Language Models on Inference Tasks
McKenna, N., Li, T., Cheng, L., Hosseini, M., Johnson, M., & Steedman, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.182 · Cited by 142
Assessing the utility of ChatGPT throughout the entire clinical workflow
Rao, A., Pang, M., Kim, J., Kamineni, M., Lie, W., Prasad, A. K., Landman, A., Dreyer, K. J., & Succi, M. D. (2023) · doi:10.1101/2023.02.21.23285886 · Cited by 134
Word Embedding and WordNet Based Metaphor Identification and Interpretation
Mao, R., Lin, C., & Guérin, F. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1113 · Cited by 132
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BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Kanakarajan, K. R., Kundumani, B., & Sankarasubbu, M. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.16 · Cited by 131
Evaluation of post-hoc interpretability methods in time-series classification
Turbé, H., Bjelogrlic, M., Lovis, C., & Mengaldo, G. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00620-w · Cited by 130
Deep Learning Techniques on Text Classification Using Natural Language Processing (NLP) In Social Healthcare Network: A Comprehensive Survey
Lavanya, P., & Sasikala, E. (2021) · doi:10.1109/icspc51351.2021.9451752 · Cited by 129
CODER: Knowledge-infused cross-lingual medical term embedding for term normalization
Yuan, Z. P., Zhao, Z., Sun, H., Li, J., Wang, F., & Yu, S. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103983 · Cited by 122
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Subramanian, S., Wang, L. L., Bogin, B., Mehta, S., van Zuylen, M., Parasa, S., Singh, S., Gardner, M., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.191 · Cited by 117
HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge
Wang, H., Liu, C., Xi, N., Qiang, Z., Zhao, S., Qin, B., & Liu, T. (2023) · arXiv · doi:10.48550/arxiv.2304.06975 · Cited by 115
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition
Schneider, E. T. R., de Souza, J. V. A., Knafou, J., Oliveira, L. E. S. E., Copara, J., Gumiel, Y. B., de Oliveira, L. F. A., Paraíso, E. C., Teodoro, D., & Barra, C. M. C. M. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.7 · Cited by 114
Evaluating the use of large language model in identifying top research questions in gastroenterology
Lahat, A., Shachar, E., Avidan, B., Shatz, Z., Glicksberg, B. S., & Klang, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-31412-2 · Cited by 111
Bidirectional Representation Learning From Transformers Using Multimodal Electronic Health Record Data to Predict Depression
Meng, Y., Speier, W. F., Ong, M. K., & Arnold, C. W. (2021) · eScholarship (California Digital Library) · Cited by 109
A Survey of Knowledge Enhanced Pre-trained Language Models
Yang, J. K., Hu, X., Xiao, G., & Shen, Y. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3631392 · Cited by 107
Text classification models for the automatic detection of nonmedical prescription medication use from social media
Al-Garadi, M. A., Yang, Y., Cai, H., Ruan, Y., O’Connor, K., Gonzalez‐Hernandez, G., Perrone, J., & Sarker, A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01394-0 · Cited by 106
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Improving Biomedical Pretrained Language Models with Knowledge
Yuan, Z. P., Liu, Y., Tan, C., Huang, S., & Huang, F. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.20 · Cited by 105
Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US
Choudhury, A., & Asan, O. (2022) · Human Factors in Healthcare · doi:10.1016/j.hfh.2022.100021 · Cited by 104
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairsPreprint
Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R. P. N., Wei, M., Valluri, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.00915 · Cited by 102
Question-driven summarization of answers to consumer health questions
Savery, M. E., Abacha, A. B., Gayen, S., & Demner‐Fushman, D. (2020) · Scientific Data · doi:10.1038/s41597-020-00667-z · Cited by 95
ChatCAD+: Toward a Universal and Reliable Interactive CAD Using LLMs
Zhao, Z., Wang, S., Gu, J., Zhu, Y., Mei, L., Zhuang, Z., Cui, Z., Wang, Q., & Shen, D. G. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3398350 · Cited by 95
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition
Naseem, U., Khushi, M., Reddy, V. L., Rajendran, S., Razzak, I., & Kim, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533884 · Cited by 88
Conceptualized Representation Learning for Chinese Biomedical Text MiningPreprint
Zhang, N., Jia, Q., Yin, K., Liang, D., Gao, F., & Hua, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10813 · Cited by 88
The promise and peril of using a large language model to obtain clinical information: ChatGPT performs strongly as a fertility counseling tool with limitations
Chervenak, J., Lieman, H., Blanco-Breindel, M., & Jindal, S. (2023) · Fertility and Sterility · doi:10.1016/j.fertnstert.2023.05.151 · Cited by 87
Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
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Explainable Metaphor Identification Inspired by Conceptual Metaphor Theory
Ge, M., Mao, R., & Cambria, E. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21313 · Cited by 83
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DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task
Xiong, H., Wang, S., Zhu, Y., Zhao, Z., Liu, Y., Huang, L., Wang, Q., & Shen, D. (2023) · arXiv · doi:10.48550/arxiv.2304.01097 · Cited by 81
HealAI: A Healthcare LLM for Effective Medical Documentation
Goyal, S., Rastogi, E., Rajagopal, S. P., Yuan, D., Zhao, F., Chintagunta, J., Naik, G., & Ward, J. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635739 · Cited by 80
A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data
Lossio-Ventura, J. A., Weger, R., Lee, A. Y., Guinee, E. P., Chung, J. Y., Atlas, L. Y., Linos, E., & Pereira, F. (2023) · JMIR Mental Health · doi:10.2196/50150 · Cited by 77
SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition
Li, W., Zhu, L., Mao, R., & Cambria, E. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26541 · Cited by 76
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On the Summarization of Consumer Health Questions
Abacha, A. B., & Demner‐Fushman, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1215 · Cited by 74
Me-LLaMA: Foundation Large Language Models for Medical ApplicationsPreprint
Xie, Q., Chen, Q., Chen, A., Cheng, P., Hu, Y., Lin, F., Peng, X., Huang, J., Zhang, J., Keloth, V. K., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4240043/v1 · Cited by 74
Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports
Bressem, K. K., Adams, L. C., Gaudin, R., Troeltzsch, D., Hamm, B., Makowski, M. R., Schuele, C. Y., Vahldiek, J. L., & Niehues, S. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa668 · Cited by 73
Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT
Naseem, U., Dunn, A. G., Khushi, M., & Kim, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04688-w · Cited by 72
Structured information extraction from complex scientific text with fine-tuned large language models
Dunn, A., Dagdelen, J., Walker, N., Lee, S., Rosen, A. S., Ceder, G., Persson, K., & Jain, A. (2022) · arXiv · doi:10.48550/arxiv.2212.05238 · Cited by 67
Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Huang, K., Singh, A., Chen, S., Moseley, E. T., Deng, C.-Y., George, N. R., & Lindvall, C. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.11 · Cited by 67
Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention
Prabhakar, S. K., & Won, D.-O. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/9425655 · Cited by 65
A medical question answering system using large language models and knowledge graphs
Guo, Q., Cao, S., & Zhang, Y. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22955 · Cited by 65
Virtual prompt pre-training for prototype-based few-shot relation extraction
He, K., Huang, Y., Mao, R., Gong, T., Li, C., & Cambria, E. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118927 · Cited by 65
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MedGPT: Medical Concept Prediction from Clinical NarrativesPreprint
Kraljević, Ž., Shek, A., Bean, D. M., Bendayan, R., Teo, J., & Dobson, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03134 · Cited by 62
Automated Lay Language Summarization of Biomedical Scientific Reviews
Guo, Y., Qiu, W., Wang, Y., & Cohen, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16089 · Cited by 61
A clinical specific BERT developed using a huge Japanese clinical text corpus
Kawazoe, Y., Shibata, D., Shinohara, E., Aramaki, E., & Ohe, K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259763 · Cited by 61
Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers
Jahan, I., Laskar, M. T. R., Peng, C., & Huang, J. X. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.30 · Cited by 58
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base
Hao, B., Zhu, H., & Paschalidis, I. C. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.57 · Cited by 57
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering CapabilityPreprint
Wang, J., Yang, Z., Yao, Z., & Yu, H. Z. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.17887 · Cited by 57
OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and DialoguePreprint
Gao, W., Deng, Z., Niu, Z., Rong, F., Chen, C. H., Gong, Z., Zhang, W., Xiao, D., Li, F., Cao, Z., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.12174 · Cited by 56
ABioNER: A BERT‐Based Model for Arabic Biomedical Named‐Entity Recognition
Boudjellal, N., Zhang, H., Khan, A., Ahmad, A., Naseem, R., Shang, J., & Dai, L. (2021) · Complexity · doi:10.1155/2021/6633213 · Cited by 54
ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation
Wang, G., Yang, G., Du, Z., Fan, L., & Li, X. (2023) · arXiv · doi:10.48550/arxiv.2306.09968 · Cited by 53
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept MappingsPreprint
Han, S., Mao, R., & Cambria, E. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.07494 · Cited by 52
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BiMediX: Bilingual Medical Mixture of Experts LLM
Pieri, S., Mullappilly, S. S., Khan, F. S., Anwer, R. M., Khan, S., Baldwin, T., & Cholakkal, H. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.989 · Cited by 52
Proceedings of the 20th Workshop on Biomedical Language Processing
(2021) · doi:10.18653/v1/2021.bionlp-1 · Cited by 50
Proceedings of the 3rd Clinical Natural Language Processing Workshop
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Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social MediaPreprint
Qin, W., Chen, Z., Wang, L., Lan, Y., Ren, W., & Hong, R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.05138 · Cited by 48
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
Poerner, N., Waltinger, U., & Schütze, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.134 · Cited by 46
Contextualized French Language Models for Biomedical Named Entity Recognition
Copara, J., Knafou, J., Naderi, N., Moro, C. M. C., Ruch, P., & Teodoro, D. (2020) · ArODES (HES-SO (https://www.hes-so.ch/)) · Cited by 46
Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture
Liu, S., Yang, H., Li, J., & Kolmanič, S. (2020) · Information · doi:10.3390/info11040186 · Cited by 46
Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT
Wang, J., Zhang, G., Wang, W., Ka, Z., & Sheng, Y. (2021) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-020-00218-2 · Cited by 45
CancerLLM: a large language model in cancer domain
Li, M., Zhan, Z., Huang, J., Yeung, J., Ding, K., Blaes, A. H., Johnson, S., Liu, H., Xu, H., & Zhang, R. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02441-8 · Cited by 42
Aloe: A Family of Fine-tuned Open Healthcare LLMsPreprint
Gururajan, A. K., Lopez-Cuena, E., Bayarri-Planas, J., Tormos, A., Hinjos, D., Bernabeu-Pérez, P., Arias-Duart, A., Martin-Torres, P. A., Ganzabal, L. U., Mallo, M. G., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.01886 · Cited by 41
Detecting Causal Language Use in Science Findings
Yu, B., Li, Y., & Wang, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1473 · Cited by 40
Transformers for Clinical Coding in Spanish
López-García, G., Jerez, J. M., Ribelles, N., Alba, E., & Veredas, F. J. (2021) · IEEE Access · doi:10.1109/access.2021.3080085 · Cited by 39
Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction
Cho, S. H., & Shin, K.-S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119390 · Cited by 38
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for MedicinePreprint
Xie, Y., Zhou, C., Gao, L., Wu, J., Li, X., Zhou, H.-Y., Liu, S., Xing, L., Zou, J., Xie, C., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.02900 · Cited by 37
Named Entity Recognition in Spanish Biomedical Literature: Short Review and Bert Model
Akhtyamova, L. (2020) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct48808.2020.9087359 · Cited by 35
ELECTRAMed: a new pre-trained language representation model for biomedical NLPPreprint
Miolo, G., Mantoan, G., & Orsenigo, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09585 · Cited by 35
PROMISE: A pre-trained knowledge-infused multimodal representation learning framework for medication recommendation
Wu, J., Yu, X., He, K., Gao, Z., & Gong, T. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103758 · Cited by 35
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Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language ModelPreprint
Ye, Q., Liu, J., Chong, D., Zhou, P., Hua, Y., Liu, F., Cao, M., Wang, Z., Cheng, X., Zhu, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.09089 · Cited by 35
Semantic matching in machine reading comprehension: An empirical study
Liu, Q., Mao, R., Geng, X., & Cambria, E. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103145 · Cited by 34
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Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups
He, K., Hong, N., Lapalme‐Remis, S., Lan, Y., Huang, M. S., Li, C., & Yao, L. (2019) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2019.02.002 · Cited by 33
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Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation
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Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives
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GatorTron: A Large Language Model for Clinical Natural Language Processing
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BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling TasksPreprint
Sharma, S., & Daniel, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05760 · Cited by 32
Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning
Mahajan, D., Poddar, A., Liang, J. J., Lin, Y.-T. R., Prager, J. M., Suryanarayanan, P., Raghavan, P., & Tsou, C.-H. (2020) · JMIR Medical Informatics · doi:10.2196/22508 · Cited by 32
Pre-training technique to localize medical BERT and enhance biomedical BERTPreprint
Wada, S., Takeda, T., Manabe, S., Konishi, S., Kamohara, J., & Matsumura, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07202 · Cited by 32
MedPix 2.0: A Comprehensive Multimodal Biomedical Data Set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs
Siragusa, I., Contino, S., Ciura, M. L., Alicata, R., & Pirrone, R. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00297-8 · Cited by 32
GenCompareSum: a hybrid unsupervised summarization method using salience
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MISS: A Generative Pre-training and Fine-Tuning Approach for Med-VQA
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Research works printed but not linked: 9

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Large Language Models in Healthcare and Medical Applications: A Review
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Application of large language models in medicine
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See all 153

Authors: He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E.
Affiliations: National University of Singapore (SG)
Nanyang Technological University (SG)
Corresponding author: Feng, Mengling
ORCID: He, Kai
Mao, Rui
Lin, Qika
Feng, Mengling
Cambria, Erik
Venue: Information Fusion
Publisher: Elsevier BV
Published: 2025-06
Volume: 118
Pages: 102963
ISSN: 1566-2535 (ISSN-L)
DOI: 10.1016/j.inffus.2025.102963
OpenAlex: W4406602253
Cited by: 147
References: 207
Citations by year: 2026: 59 · 2025: 83
FWCI: 34.18
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Information Fusion (publishedVersion, cc-by)
DR-NTU (Nanyang Technological University) (submittedVersion)
Funders: National Research Foundation (AISG-GC-2019-001-2A, AISG2-TC-2022-004)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: large language models, medical applications, pre-trained language models, generative AI, fairness, accountability, transparency, AI ethics, open source resources

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A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

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