Large language models (LLMs)

Health Management Glossary

Digital Health and AI

Definition. Large language models are AI systems trained on very large text collections to predict and generate language, which allows them to answer questions, summarise documents and draft text.

Explanation

In health care, LLMs are tested for documentation, patient communication, education and specialised tasks such as assessing causality in adverse drug reaction reports (Abate et al., 2025). In universities, they can be built into teaching to scaffold rather than replace critical thinking (Vendrell & Johnston, 2026).

Their limits are well documented: they can produce fluent but wrong answers, reflect stereotypes and, in simulations, slip into caricature of the groups they imitate (Cheng et al., 2023). Safe use therefore requires task-specific evaluation, human verification of outputs, protection of patient data and transparent disclosure when LLMs contribute to clinical or academic work.

Related concepts

Artificial intelligence (AI) Algorithmic bias AI governance Clinical decision support system Peer review

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References

Abate, A., Poncato, E., Barbieri, M. A., Powell, G., Rossi, A., Peker, S., … Sessa, M. (2025). Off-the-shelf large language models for causality assessment of individual case safety reports: A proof-of-concept with COVID-19 vaccines. Drug Safety, 48 (7), 805–820. https://doi.org/10.1007/s40264-025-01531-y

Cheng, M., Piccardi, T., & Yang, D. (2023). CoMPosT: Characterizing and evaluating caricature in LLM simulations. In H.

Vendrell, M., & Johnston, S.-K. (2026). Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education. Computers and Education: Artificial Intelligence. Advance online publication. https://doi.org/10.1016/j.caeai.2026.100572

Cite this entry: Kurutkan, M. N. (2026). Large language models (LLMs). In The Health Topics glossary. https://healthtopic.org/glossary/large-language-models/

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