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Opportunities and challenges for ChatGPT and large language models in biomedicine and health
Open accessAbstract
ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.
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“Prompt engineering improves chatbot performance by tailoring LLM output to be focused and helpful for a specific task.162,163”
From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [162]
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Abdelmalek, G., Uppal, H., Garcia, D. J., Farshchian, J., Emami, A., & McGinniss, A. (2024) · Journal of Hand Surgery Global Online · doi:10.1016/j.jhsg.2024.10.002 · Cited by 26
Privacy-preserving LLM-based chatbots for hypertensive patient self-management
Montagna, S., Ferretti, S., Klopfenstein, L. C., Ungolo, M., Pengo, M. F., Aguzzi, G., & Magnini, M. (2025) · Smart Health · doi:10.1016/j.smhl.2025.100552 · Cited by 26
Perspectives and Experiences With Large Language Models in Health Care: Survey Study
Sumner, J., Wang, Y., Tan, S. Y., Chew, E. H. H., & Yip, A. W. (2025) · Journal of Medical Internet Research · doi:10.2196/67383 · Cited by 25
ChatGPT in Medical Education: Bibliometric and Visual Analysis
Zhang, Y., Xie, X., & Xu, Q. (2025) · JMIR Medical Education · doi:10.2196/72356 · Cited by 35
Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study
Liu, S., Wang, A., Xiu, X., Zhong, M., & Wu, S. (2024) · JMIR Medical Informatics · doi:10.2196/59782 · Cited by 23
Large language models for bioinformatics
Ruan, W., Lyu, Y., Zhang, J., Cai, J., Shu, P., Yang, G., Lu, Y., Gao, S., Wang, Y., Wang, P., et al. (2025) · Quantitative Biology · doi:10.1002/qub2.70014 · Cited by 37
A survey on biomedical automatic text summarization with large language models
Huang, Z., Chen, X., Wang, Y., Huang, J., & Zhao, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104216 · Cited by 24
Self-citationHuang, L., Zhang, X., Wang, Q., Chen, Z., & Liu, Y. (2024) · Journal of the Knowledge Economy · doi:10.1007/s13132-024-01755-w · Cited by 22
A comprehensive survey for automatic text summarization: Techniques, approaches and perspectives
Luo, M., Xue, B., & Niu, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128280 · Cited by 25
Bibliometric Analysis on ChatGPT Research with CiteSpace
Nan, D., Zhao, X., Chen, C., Sun, S., Lee, K. R., & Kim, J. H. (2025) · Information · doi:10.3390/info16010038 · Cited by 26
Adversarial prompt and fine-tuning attacks threaten medical large language models
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Self-citationZero-shot evaluation of ChatGPT for food named-entity recognition and linking
Ogrinc, M., Seljak, B. K., & Eftimov, T. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1429259 · Cited by 19
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Carchiolo, V., & Malgeri, M. (2025) · Future Internet · doi:10.3390/fi17020076 · Cited by 21
Evaluating the quality of medical content on YouTube using large language models
Khalil, M., Mohamed, F., & Shoufan, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-94208-6 · Cited by 24
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A perspective for adapting generalist AI to specialized medical AI applications and their challenges
Wang, Z., Wang, H., Danek, B. P., Li, Y., Mack, C. D., Arbuckle, L., Biswal, D., Poon, H., Wang, Y., Rajpurkar, P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01789-7 · Cited by 35
Mapping the individual, social and biospheric impacts of Foundation Models
Hernández, A. D., Krishna, S., Perini, A., Katell, M. A., Bennett, S., Borda, A., Hashem, Y., Hadjiloizou, S., Mahomed, S., Jayadeva, S., et al. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658939 · Cited by 38
Algorethics in Healthcare: Balancing Innovation and Integrity in AI Development
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“Generative large language models (LLMs) based on Transformer architecture 45 (e.g., GPT family 46,47 , PaLM 48 , LLaMA 49 ) have shown remarkable language generation capabilities and are increasingly applied to NLP tasks in the general domain 50 and in the biomedical domain 51,52 .”
BackgroundMethodInfluentialAkbasli, I. T., Bırbılen, A. Z., & Tekşam, Ö. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02871-6 · Cited by 18
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Chatzopoulos, G. S., Koidou, V. P., Tsalikis, L., & Kaklamanos, E. G. (2025) · Dentistry Journal · doi:10.3390/dj13060271 · Cited by 16
Metadata
- Authors
- Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Y., Chen, Q., Kim, W., Comeau, D. C., Islamaj, R., Kapoor, A., Gao, X., & Lu, Z.
- Affiliations
- United States National Library of Medicine (US)
- King Abdullah University of Science and Technology (SA)
- Corresponding author
- Lu, Zhiyong
- ORCID
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-11-22
- Volume
- 25
- Issue
- 1
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC10762511
- OpenAlex
- W4381587418
- Cited by
- 359
- References
- 132
- Citations by year
- 2026: 81 · 2025: 161 · 2024: 112 · 2023: 5
- FWCI
- 14.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Briefings in Bioinformatics (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- NIH Intramural Research Program
- National Library of Medicine
- National Institutes of Health (1K99LM014024)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- ChatGPT, large language models, biomedical information retrieval, question answering, medical text summarization, information extraction, medical education, patient data privacy, legal issues, text generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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