Open Access
Infectious Diseases and Therapy
Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape
Daniele R. Giacobbe
1, 2
,
Sabrina Guastavino
3
,
Cristina Marelli
4, 5
,
Ylenia Murgia
6
,
Sara Mora
7
,
Alessio Signori
8, 9
,
Nicola Rosso
7
,
Mauro Giacomini
6
,
Cristina Campi
3, 10
,
Michele Piana
3, 10
,
Matteo Bassetti
1, 2
4
Institut Curie, INSERM U1331 Team Statistics Applied to Personalized Medicine, Paris, France
5
Gustave Roussy, INSERM CESP Team OncoStat, University Paris Saclay, Villejuif, France
7
8
Publication type: Journal Article
Publication date: 2025-02-15
Journal:
Infectious Diseases and Therapy
scimago Q1
SJR: 1.351
CiteScore: 8.6
Impact factor: 4.7
ISSN: 21938229, 21936382
Abstract
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.
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