Implementing large language models in healthcare while balancing control, collaboration, costs and security
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.
Authors
- Paul Martin Putora (ORCID: https://orcid.org/0000-0002-4972-6115)
- Nikola Čihorić (ORCID: https://orcid.org/0000-0003-1534-8520)
- Fabio Dennstädt (ORCID: https://orcid.org/0000-0002-5374-8720)
- Janna Hastings (ORCID: https://orcid.org/0000-0002-3469-4923)
- Max Schmerder (ORCID: https://orcid.org/0009-0009-3663-4412)
Institutions
- University of Bern (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- University of Zurich (CH)
- University of St.Gallen (CH)
- Kantonsspital St. Gallen (CH)
- University Hospital of Bern (CH)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1038/s41746-025-01476-7
- Citations
- 72
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 47.11