Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape
The growing interest in leveraging artificial intelligence (AI) tools for healthcare decision-making extends to improving antibiotic prescribing. Large language models (LLMs), a type of AI trained on extensive datasets from diverse sources, can process and generate contextually relevant text. While their potential to enhance patient outcomes is significant, implementing LLM-based support for antibiotic prescribing is complex. Here, we specifically expand the discussion on this crucial topic by introducing three interconnected perspectives: (1) the distinctive commonalities, but also the crucial conceptual differences, between the use of LLMs as assistants in scientific writing and in supporting antibiotic prescribing in real-world practice; (2) the possibility and nuances of the expertise paradox; and (3) the peculiarities of the risk of error when considering LLMs to support complex tasks such as antibiotic prescribing.
Authors
- Nicola Rosso
- Alessio Signori (ORCID: https://orcid.org/0000-0001-6289-9144)
- Matteo Bassetti (ORCID: https://orcid.org/0000-0002-0145-9740)
- Mauro Giacomini (ORCID: https://orcid.org/0000-0001-5646-2034)
- Sara Mora (ORCID: https://orcid.org/0000-0002-0613-3248)
- Sabrina Guastavino (ORCID: https://orcid.org/0000-0001-7047-1148)
- Daniele Roberto Giacobbe (ORCID: https://orcid.org/0000-0003-2385-1759)
- Cristina Campi (ORCID: https://orcid.org/0000-0003-2105-8554)
- Cristina Marelli (ORCID: https://orcid.org/0000-0003-1510-1819)
- Michele Piana (ORCID: https://orcid.org/0000-0003-1700-991X)
- Ylenia Murgia (ORCID: https://orcid.org/0009-0009-3303-3160)
Institutions
- Inserm (FR)
- Institut Gustave Roussy (FR)
- Centre de recherche en Epidémiologie et Santé des Populations (FR)
- Ospedale Policlinico San Martino (IT)
- Institut Curie (FR)
- University of Genoa (IT)
Publication Details
- Journal
- Infectious Diseases and Therapy
- Published
- 2025-02-15
- DOI
- https://doi.org/10.1007/s40121-025-01114-5
- Citations
- 4
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.54