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.

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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
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Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape

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4 citations
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Cell Image Analysis Techniques
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Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape

Nicola Rosso, Alessio Signori, Matteo Bassetti, Mauro Giacomini, Sara Mora, Sabrina Guastavino, Daniele Roberto Giacobbe, Cristina Campi, Cristina Marelli, Michele Piana, Ylenia Murgia
article en
4 citations

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.

Infectious Diseases and TherapyVol. 14(3)
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)
Openalex Percentile: Top 4%
Cell Image Analysis Techniques
6.54
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