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.
“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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A Critical Review of ChatGPT as a Potential Substitute for Diabetes Educators
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Large language models in healthcare and medical domain: A review
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“For instance, few studies evaluated more advanced closed-source LLMs such as GPT-4, LLM representatives from the general domain such as LLaMA 22 , and bio-medical domain-speci fi c LLMs such as PMC-LLaMA 34 .”
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Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
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Recent Advances in Large Language Models for Healthcare
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Unmasking and quantifying racial bias of large language models in medical report generation
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Self-citationApplication of large language models in medicine
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GeneAgent: self-verification language agent for gene-set analysis using domain databases
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Self-citationIntroduction to large language models (LLMs) for dementia care and research
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Small language models learn enhanced reasoning skills from medical textbooks
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Self-citationLarge Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis
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Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis
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Can ChatGPT Perform a Grounded Theory Approach to Do Risk Analysis? An Empirical Study
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Role and challenges of ChatGPT and similar generative artificial intelligence in arts and humanities
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Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness
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A Framework for Assessing the Potential of Artificial Intelligence in the Circular Bioeconomy
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Benchmarking Large Language Models in Evidence-Based Medicine
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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 .”
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- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › 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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