A head-to-head comparison of the accuracy of commercially available large language models for infection prevention and control inquiries, 2024
We investigated the accuracy and completeness of four large language model (LLM) artificial intelligence tools. Most LLMs provided acceptable answers to commonly asked infection prevention questions (accuracy 98.9%, completeness 94.6%). The use of LLMs to supplement infection prevention consults should be further explored.
Authors
- Guillermo Rodríguez-Nava (ORCID: https://orcid.org/0000-0001-9826-7050)
- Takaaki Kobayashi (ORCID: https://orcid.org/0000-0003-4751-6859)
- Jorge Salinas (ORCID: https://orcid.org/0000-0002-9193-820X)
- Oluchi Abosi (ORCID: https://orcid.org/0000-0003-1746-6344)
- Karen Brust (ORCID: https://orcid.org/0000-0001-5853-2323)
- Alexandra Trannel (ORCID: https://orcid.org/0000-0001-6020-592X)
- Natalie Ross
Institutions
- University of Iowa Health Care (US)
- Stanford University (US)
Publication Details
- Journal
- Infection Control and Hospital Epidemiology
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1017/ice.2024.205
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.24