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Cited by 149Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareEthics and Social Impacts of AI

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Implementing large language models in healthcare while balancing control, collaboration, costs and security

Open access
Dennstädt, Fabio, Hastings, Janna, Putora, Paul Martin, Schmerder, Max, Cihoric, Nikola·2025·npj Digital Medicine
DOI 10.1038/s41746-025-01476-7PMC PMC11885444
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Abstract

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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Fig. 1 Closed vs. open approach of deploying LLMs for clinical applications. a A closed LLM runs on an external server of a private company. The LLM is controlled by the commercial partner. b An open LLM runs within the local environment of the healthcare facility. Private companies may be referred to for setting up and maintaining the local infrastructure. The LLM is controlled by the healthcare facility.
Fig. 1 Closed vs. open approach of deploying LLMs for clinical applications. a A closed LLM runs on an external server of a private company. The LLM is controlled by the commercial partner. b An open LLM runs within the local environment of the healthcare facility. Private companies may be referred to for setting up and maintaining the local infrastructure. The LLM is controlled by the healthcare facility.CC BY-NC-ND · PubMed Central
Fig. 2 Different clinical tasks to use LLMs. Schematic illustration of various clinical tasks where LLMs may be utilized, based on the level of sensitive data required and level of technical requirements.
Fig. 2 Different clinical tasks to use LLMs. Schematic illustration of various clinical tasks where LLMs may be utilized, based on the level of sensitive data required and level of technical requirements.CC BY-NC-ND · PubMed Central

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Research works printed but not linked: 1

  1. The HIPAA Journal: Managed care of North America hacking incident impacts 8.9 million individuals. http://www.hipaajournal.com/managed-care-of-north-america-hacking-incident-impacts-8-9-million-individuals/.

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Metadata

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
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2025-03-06
Volume
8
Issue
1
Pages
143-143
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-025-01476-7
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
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
  • Physical Sciences › Computer Science › Artificial Intelligence › 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

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