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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月15日

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

摘要

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.