Implementing large language models in healthcare while balancing control, collaboration, costs and security
Open accessAbstract
Integrating Large Language Models (LLMs) into healthcare promises substantial advancements but requires careful consideration of technical, ethical, and regulatory challenges. Closed LLMs of private companies offer ease of deployment but pose risks related to data privacy and vendor dependence. Open LLMs deployed on local hardware enable greater model customization but demand resources and technical expertise. Balancing these approaches, with collaboration among clinicians, researchers, and companies is crucial to ensure effective, secure, and ethical implementation.
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Deploying large language models in clinical settings is limited by security, reliability, latency, and accessibility concerns that favor smaller, on-device or on-premise models.
“Deploying large language models (LLMs) in clinical settings is limited by security, reliability, latency, and accessibility concerns that favor smaller, on-device or on-premise models.”
From The economics of accuracy for medical reasoning with large language models (2026) · cites this paper as [3]
The “Closed vs. open LLMs” section says that hosting models within a healthcare facility can reduce data-security risks and provide more control over outputs. It does not identify latency as a concern or recommend smaller, on-device models; it also says the choice between local and API-based systems depends on the task. (LLM, conf=0.87)
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- Authors
- Dennstädt, F., Hastings, J., Putora, P. M., Schmerder, M., & Cihoric, N.
- Affiliations
- University of Bern (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Dennstädt, Fabio
- Venue
- npj Digital Medicine
- Published
- 2025-03-06
- Volume
- 8
- Issue
- 1
- Pages
- 143-143
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11885444
- OpenAlex
- W4408197148
- Cited by
- 144
- References
- 14
- Citations by year
- 2026: 90 · 2025: 54
- FWCI
- 34.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Digital Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open Access CRIS of the University of Bern
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- large language models, data privacy, open-source LLMs, model customization, AI security, ethical considerations, regulatory challenges, clinician collaboration
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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