Built on

Jin, Q., et al.: PubMedQA: A dataset for biomedical research question answering. (2019). https://arxiv.org/abs/1909.06146
Original
1909
Earlier work this paper cites.
Lewis, M., et al.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. (2019). https://arxiv.org/abs/1910.13461
Original
1910
Earlier work this paper cites.
He, X., et al.: PathVQA: 30000+ questions for medical visual question answering. (2020). https://arxiv.org/abs/2003.10286
Original
2003
Earlier work this paper cites.
Banerjee, S., Lavie, A.: Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In: Proceedings of the Acl Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation And/or Summarization, pp. 65–72 (2005)
2005
Earlier work this paper cites.
Hendrycks, D., et al.: Measuring massive multitask language understanding. (2021). https://arxiv.org/abs/2009.03300
Original
2009
Earlier work this paper cites.
Benson, D.A., et al
2012
Earlier work this paper cites.
Herrero-Zazo, M., et al
2013
Earlier work this paper cites.
Ba, J.L., et al.: Layer Normalization (2016). https://arxiv.org/abs/1607.06450
Original
2016
Earlier work this paper cites.
Demner-Fushman, D., et al
2016
Earlier work this paper cites.
Li, J., et al.: Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database 2016
2016
Earlier work this paper cites.
Johnson, A.E.W., et al
2016
Earlier work this paper cites.
Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
2016
Earlier work this paper cites.
MacParland, S.A., et al
2018
Earlier work this paper cites.
Lau, J.J., et al
2018
Earlier work this paper cites.
Zhang, S., et al
2018
Earlier work this paper cites.
Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25
2019
Earlier work this paper cites.
Devlin, J., et al.: BERT: Pre-training of deep bidirectional transformers for language understanding. (2019). https://arxiv.org/abs/1810.04805
Original
2019
Earlier work this paper cites.
Houssein, A., et al.: BMC Biomedical Engineering: a home for all biomedical engineering research. Springer (2019)
2019
Earlier work this paper cites.
Houlsby, N., et al
2019
Earlier work this paper cites.
Johnson, A.E.W., et al
2019
Earlier work this paper cites.
Franzén, O., et al
2019
Earlier work this paper cites.
He, J., et al
2019
Earlier work this paper cites.
Brown, T., et al
2020
Earlier work this paper cites.
Lee, J., et al
2020
Earlier work this paper cites.
Raffel, C., et al
2020
Earlier work this paper cites.
Wang, Y., et al
2020
Earlier work this paper cites.
Wu, X., Liang, Z., Wang, J.: Fedmed: A federated learning framework for language modeling. Sensors 20
2020
Earlier work this paper cites.
Radford, A., et al
2021
Earlier work this paper cites.
Ramesh, A., et al
2021
Earlier work this paper cites.
Jin, D., et al
2021
Earlier work this paper cites.
Hu, E.J., et al.: LoRA: Low-rank adaptation of large language models. (2021). https://arxiv.org/abs/2106.09685
Original
2021
Earlier work this paper cites.
Zhang, C., et al
2021
Earlier work this paper cites.
Liu, B., et al
2021
Earlier work this paper cites.
Yang, F., et al
2022
Earlier work this paper cites.
Luo, R., et al
2022
Earlier work this paper cites.
Du, Z., et al.: GLM: General language model pretraining with autoregressive blank infilling. (2022). https://arxiv.org/abs/2103.10360
Original
2022
Earlier work this paper cites.
Yuan, H., et al.: BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model (2022). https://arxiv.org/abs/2204.03905
Original
2022
Earlier work this paper cites.
Wu, S., et al
2022
Earlier work this paper cites.
Ward, M., et al.: A quantitative assessment of chatgpt as a neurosurgical triaging tool. Neurosurgery, 10–1227 (2022)
2022
Earlier work this paper cites.
Aldahdooh, W., et al
2022
Earlier work this paper cites.
Pal, A., et al
2022
Earlier work this paper cites.
Luo, L., et al
2022
Earlier work this paper cites.
Yang, X., et al
2022
Earlier work this paper cites.
Bai, Y., et al.: Constitutional ai: Harmlessness from ai feedback (2022) arXiv:2212.08073 [cs.CL]
Original
2022
Earlier work this paper cites.
Weller, O., et al.: Pretrained models for multilingual federated learning, 1413–1421 (2022)
2022
Earlier work this paper cites.
Chowdhery, A., et al
2023
Earlier work this paper cites.
Touvron, H., et al.: LLaMA: Open and efficient foundation language models. (2023). https://arxiv.org/abs/2302.13971
Original
2023
Earlier work this paper cites.
Touvron, H., et al.: Llama 2: Open foundation and fine-tuned chat models. (2023). https://arxiv.org/abs/2307.09288
Original
2023
Earlier work this paper cites.
Hadi, M.U., et al.: Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects. Authorea Preprints (2023)
2023
Earlier work this paper cites.
Singhal, K., et al.: Towards expert-level medical question answering with large language models. (2023). https://arxiv.org/abs/2305.09617
Original
2023
Earlier work this paper cites.

Similar

Zhang, H., et al.: HuatuoGPT, towards taming language model to be a doctor. (2023). https://arxiv.org/abs/2305.15075
Original
2023
Cited alongside, same era.
Li, Y., et al.: Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge. Cureus 15
2023
Cited alongside, same era.
Wang, H., et al.: HuaTuo: Tuning LLaMA model with Chinese medical knowledge. (2023). https://arxiv.org/abs/2304.06975
Original
2023
Cited alongside, same era.
Sallam, M.: Chatgpt utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. In: Healthcare, vol. 11, p. 887 (2023). MDPI
2023
Cited alongside, same era.
Xie, Q., et al.: Faithful ai in medicine: A systematic review with large language models and beyond. medRxiv (2023)

Then

Naveed, H., et al.: A comprehensive overview of large language models. (2024). https://arxiv.org/abs/2307.06435
Original
2024
Closest in time.
Zhou, H., et al.: A survey of large language models in medicine: progress, application, and challenge. (2024). https://arxiv.org/abs/2311.05112
Original
2024
Closest in time.
Mumtaz, U., et al
2024
Closest in time.
Bolton, E., et al.: BioMedLM: A 2.7B parameter language model trained on biomedical text. (2024). https://arxiv.org/abs/2403.18421
Original
2024
Closest in time.
Minaee, S., et al.: Large language models: A survey. (2024). https://arxiv.org/abs/2402.06196
Original
2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2024

A Survey for Large Language Models in Biomedicine

Wang, Chong, Li, Mengyao, He, Junjun et al.

Understand

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities.

However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains.
This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts.
Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies.

Built on

Jin, Q., et al.: PubMedQA: A dataset for biomedical research question answering. (2019). https://arxiv.org/abs/1909.06146
Original
1909
Earlier work this paper cites.
Lewis, M., et al.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. (2019). https://arxiv.org/abs/1910.13461
Original
1910
Earlier work this paper cites.
He, X., et al.: PathVQA: 30000+ questions for medical visual question answering. (2020). https://arxiv.org/abs/2003.10286
Original
2003
Earlier work this paper cites.
Banerjee, S., Lavie, A.: Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In: Proceedings of the Acl Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation And/or Summarization, pp. 65–72 (2005)
2005
Earlier work this paper cites.
Hendrycks, D., et al.: Measuring massive multitask language understanding. (2021). https://arxiv.org/abs/2009.03300
Original
2009
Earlier work this paper cites.
Benson, D.A., et al
2012
Earlier work this paper cites.
Herrero-Zazo, M., et al
2013
Earlier work this paper cites.
Ba, J.L., et al.: Layer Normalization (2016). https://arxiv.org/abs/1607.06450
Original
2016
Earlier work this paper cites.
Demner-Fushman, D., et al
2016
Earlier work this paper cites.
Li, J., et al.: Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database 2016
2016
Earlier work this paper cites.
Johnson, A.E.W., et al
2016
Earlier work this paper cites.
Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
2016
Earlier work this paper cites.
MacParland, S.A., et al
2018
Earlier work this paper cites.
Lau, J.J., et al
2018
Earlier work this paper cites.
Zhang, S., et al
2018
Earlier work this paper cites.
Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25
2019
Earlier work this paper cites.
Devlin, J., et al.: BERT: Pre-training of deep bidirectional transformers for language understanding. (2019). https://arxiv.org/abs/1810.04805
Original
2019
Earlier work this paper cites.
Houssein, A., et al.: BMC Biomedical Engineering: a home for all biomedical engineering research. Springer (2019)
2019
Earlier work this paper cites.
Houlsby, N., et al
2019
Earlier work this paper cites.
Johnson, A.E.W., et al
2019
Earlier work this paper cites.
Franzén, O., et al
2019
Earlier work this paper cites.
He, J., et al
2019
Earlier work this paper cites.
Brown, T., et al
2020
Earlier work this paper cites.
Lee, J., et al
2020
Earlier work this paper cites.
Raffel, C., et al
2020
Earlier work this paper cites.
Wang, Y., et al
2020
Earlier work this paper cites.
Wu, X., Liang, Z., Wang, J.: Fedmed: A federated learning framework for language modeling. Sensors 20
2020
Earlier work this paper cites.
Radford, A., et al
2021
Earlier work this paper cites.
Ramesh, A., et al
2021
Earlier work this paper cites.
Jin, D., et al
2021
Earlier work this paper cites.
Hu, E.J., et al.: LoRA: Low-rank adaptation of large language models. (2021). https://arxiv.org/abs/2106.09685
Original
2021
Earlier work this paper cites.
Zhang, C., et al
2021
Earlier work this paper cites.
Liu, B., et al
2021
Earlier work this paper cites.
Yang, F., et al
2022
Earlier work this paper cites.
Luo, R., et al
2022
Earlier work this paper cites.
Du, Z., et al.: GLM: General language model pretraining with autoregressive blank infilling. (2022). https://arxiv.org/abs/2103.10360
Original
2022
Earlier work this paper cites.
Yuan, H., et al.: BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model (2022). https://arxiv.org/abs/2204.03905
Original
2022
Earlier work this paper cites.
Wu, S., et al
2022
Earlier work this paper cites.
Ward, M., et al.: A quantitative assessment of chatgpt as a neurosurgical triaging tool. Neurosurgery, 10–1227 (2022)
2022
Earlier work this paper cites.
Aldahdooh, W., et al
2022
Earlier work this paper cites.
Pal, A., et al
2022
Earlier work this paper cites.
Luo, L., et al
2022
Earlier work this paper cites.
Yang, X., et al
2022
Earlier work this paper cites.
Bai, Y., et al.: Constitutional ai: Harmlessness from ai feedback (2022) arXiv:2212.08073 [cs.CL]
Original
2022
Earlier work this paper cites.
Weller, O., et al.: Pretrained models for multilingual federated learning, 1413–1421 (2022)
2022
Earlier work this paper cites.
Chowdhery, A., et al
2023
Earlier work this paper cites.
Touvron, H., et al.: LLaMA: Open and efficient foundation language models. (2023). https://arxiv.org/abs/2302.13971
Original
2023
Earlier work this paper cites.
Touvron, H., et al.: Llama 2: Open foundation and fine-tuned chat models. (2023). https://arxiv.org/abs/2307.09288
Original
2023
Earlier work this paper cites.
Hadi, M.U., et al.: Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects. Authorea Preprints (2023)
2023
Earlier work this paper cites.
Singhal, K., et al.: Towards expert-level medical question answering with large language models. (2023). https://arxiv.org/abs/2305.09617
Original
2023
Earlier work this paper cites.

Similar

Zhang, H., et al.: HuatuoGPT, towards taming language model to be a doctor. (2023). https://arxiv.org/abs/2305.15075
Original
2023
Cited alongside, same era.
Li, Y., et al.: Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge. Cureus 15
2023
Cited alongside, same era.
Wang, H., et al.: HuaTuo: Tuning LLaMA model with Chinese medical knowledge. (2023). https://arxiv.org/abs/2304.06975
Original
2023
Cited alongside, same era.
Sallam, M.: Chatgpt utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. In: Healthcare, vol. 11, p. 887 (2023). MDPI
2023
Cited alongside, same era.
Xie, Q., et al.: Faithful ai in medicine: A systematic review with large language models and beyond. medRxiv (2023)

Then

Naveed, H., et al.: A comprehensive overview of large language models. (2024). https://arxiv.org/abs/2307.06435
Original
2024
Closest in time.
Zhou, H., et al.: A survey of large language models in medicine: progress, application, and challenge. (2024). https://arxiv.org/abs/2311.05112
Original
2024
Closest in time.
Mumtaz, U., et al
2024
Closest in time.
Bolton, E., et al.: BioMedLM: A 2.7B parameter language model trained on biomedical text. (2024). https://arxiv.org/abs/2403.18421
Original
2024
Closest in time.
Minaee, S., et al.: Large language models: A survey. (2024). https://arxiv.org/abs/2402.06196
Original
2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2023

Cited alongside, same era.

Peng, C., et al

2023

Cited alongside, same era.

Chen, Y.R., et al.: BianQue: Balancing the questioning and suggestion ability of health LLMs with multi-turn health conversations polished by ChatGPT. (2023). https://arxiv.org/abs/2310.15896

Original

2023

Cited alongside, same era.

Bao, Z.J., et al.: DISC-MedLLM: Bridging general large language models and real-world medical consultation. (2023). https://arxiv.org/abs/2308.14346

Original

2023

Cited alongside, same era.

Singhal, K., et al

2023

Cited alongside, same era.

Yang, X., et al.: Genecompass: Deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model. bioRxiv, 2023–09 (2023)

2023

Cited alongside, same era.

Liu, J., et al

2023

Cited alongside, same era.

Kumar, Y., et al

2023

Cited alongside, same era.

Li, T., et al

2024

Closest in time.

Li, J., et al.: Chatgpt in healthcare: a taxonomy and systematic review. Comput. Methods Programs Biomed., 108013 (2024)

2024

Closest in time.

Deng, L., et al

2024

Closest in time.

Shen, J., et al

2024

Closest in time.

Hou, W., Ji, Z., et al

2024

Closest in time.

Zhang, L., et al

2024

Closest in time.

Ferrario, A., et al.: Large language models in medical ethics: useful but not expert. J. Med. Ethics (2024)

2024

Closest in time.

Sandmann, S., et al

2024

Closest in time.

Meng, X., et al.: The application of large language models in medicine: A scoping review. Iscience 27

2024

Closest in time.

Tu, T., et al

2024

Closest in time.

Qiu, P., et al.: Towards building multilingual language model for medicine. (2024). https://arxiv.org/abs/2402.13963

Original

2024

Closest in time.

Yang, S., et al

2024

Closest in time.

Liu, J., et al.: Benchmarking large language models on cmexam—a comprehensive chinese medical exam dataset. Adv. Neural Inf. Process. Syst. 36

2024

Closest in time.

Shen, Y., Chen, Z., Mamalakis, M., He, L., Xia, H., Li, T., Su, Y., He, J., Wang, Y.G.: A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding (2024). https://arxiv.org/abs/2406.05540

Original

2024

Closest in time.

Horiuchi, D., et al.: Comparing the diagnostic performance of gpt-4-based chatgpt, gpt-4v-based chatgpt, and radiologists in challenging neuroradiology cases. Clinical Neuroradiology, 1–9 (2024)

2024

Closest in time.

Du, X., et al.: Generative large language models in electronic health records for patient care since 2023: A systematic review. medRxiv, 2024–08 (2024)

2024

Closest in time.

Zhou, Z., et al.: DNABERT-S: Learning species-aware DNA embedding with genome foundation models. (2024). https://arxiv.org/abs/2402.08777

Original

2024

Closest in time.

Tan, Y., et al

2024

Closest in time.

Liu, Z., et al.: Radiology-GPT: A large language model for radiology. (2024). https://arxiv.org/abs/2306.08666

Original

2024

Closest in time.

Cui, H., et al.: scgpt: toward building a foundation model for single-cell multi-omics using generative ai. Nature Methods, 1–11 (2024)

2024

Closest in time.

Luo, L., et al.: Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks. J. Am. Med. Inform. Assoc., 037 (2024)

2024

Closest in time.

Guthrie, E., et al.: The operating and anesthetic reference assistant (oara): A fine-tuned large language model for resident teaching. Am. J. Surg. (2024)

2024

Closest in time.

Wang, Z., et al.: Towards training a Chinese large language model for anesthesiology. (2024). https://arxiv.org/abs/2403.02742

Original

2024

Closest in time.

Ye, Q., et al.: Qilin-Med: Multi-stage knowledge injection advanced medical large language model. (2024). https://arxiv.org/abs/2310.09089

Original

2024

Closest in time.

Zhang, X., et al.: AlpaCare: Instruction-tuned large language models for medical application. (2024). https://arxiv.org/abs/2310.14558

Original

2024

Closest in time.

Shoham, O.B., et al.: CPLLM: Clinical prediction with large language models. (2024). https://arxiv.org/abs/2309.11295

Original

2024

Closest in time.

Abramson, J., et al.: Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, 1–3 (2024)

2024

Closest in time.

Ma, J., et al

2024

Closest in time.

Zhang, S., et al.: BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. (2024). https://arxiv.org/abs/2303.00915

Original

2024

Closest in time.

Kim, C., et al.: Transparent medical image ai via an image–text foundation model grounded in medical literature. Nature Med., 1–12 (2024)

2024

Closest in time.

Christensen, M., et al.: Vision–language foundation model for echocardiogram interpretation. Nature Med., 1–8 (2024)

2024

Closest in time.

Ji, J., et al

2024

Closest in time.

Saab, K., et al.: Capabilities of gemini models in medicine. (2024). https://arxiv.org/abs/2404.18416

Original

2024

Closest in time.

Shen, Y., Lv, O., Zhu, H., Wang, Y.G.: ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering (2024). https://arxiv.org/abs/2405.06658

Original

2024

Closest in time.

Shen, J., et al.: Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains (2024). https://arxiv.org/abs/2402.05140

Original

2024

Closest in time.

D., L., et al.: AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models (2024). https://arxiv.org/abs/2408.00665

Original

2024

Closest in time.

Nam, Y., et al.: Harnessing artificial intelligence in multimodal omics data integration: Paving the path for the next frontier in precision medicine. Annual Review of Biomedical Data Science 7

2024

Closest in time.

Shen, Y., Chen, Z., Mamalakis, M., Liu, Y., Li, T., Su, Y., He, J., Liò, P., Wang, Y.G.: TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering (2024). https://arxiv.org/abs/2408.15299

Original

2024

Closest in time.

Zhang, S., et al.: Instruction tuning for large language models: A survey. (2024). https://arxiv.org/abs/2308.10792

Original

2024

Closest in time.

Zhao, F., et al

2024