抗生素和人工智能:在一个快速发展的景观临床考虑。

IF 4.7 3区 医学 Q1 INFECTIOUS DISEASES
Infectious Diseases and Therapy Pub Date : 2025-03-01 Epub Date: 2025-02-15 DOI:10.1007/s40121-025-01114-5
Daniele Roberto Giacobbe, Sabrina Guastavino, Cristina Marelli, Ylenia Murgia, Sara Mora, Alessio Signori, Nicola Rosso, Mauro Giacomini, Cristina Campi, Michele Piana, Matteo Bassetti
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引用次数: 0

摘要

人们对利用人工智能(AI)工具进行医疗保健决策的兴趣日益浓厚,并扩展到改进抗生素处方。大型语言模型(llm)是一种基于来自不同来源的广泛数据集进行训练的人工智能,可以处理和生成与上下文相关的文本。虽然它们在提高患者预后方面的潜力是巨大的,但实施基于法学硕士的抗生素处方支持是复杂的。在这里,我们通过引入三个相互关联的观点来具体扩展对这一关键主题的讨论:(1)在现实世界的实践中,使用法学硕士作为科学写作助手和支持抗生素处方之间的独特共同点,以及关键的概念差异;(2)专家悖论的可能性和细微差别;(3)在考虑llm支持复杂任务(如抗生素处方)时,错误风险的特殊性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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来源期刊
Infectious Diseases and Therapy
Infectious Diseases and Therapy Medicine-Microbiology (medical)
CiteScore
8.60
自引率
1.90%
发文量
136
审稿时长
6 weeks
期刊介绍: Infectious Diseases and Therapy is an international, open access, peer-reviewed, rapid publication journal dedicated to the publication of high-quality clinical (all phases), observational, real-world, and health outcomes research around the discovery, development, and use of infectious disease therapies and interventions, including vaccines and devices. Studies relating to diagnostic products and diagnosis, pharmacoeconomics, public health, epidemiology, quality of life, and patient care, management, and education are also encouraged. Areas of focus include, but are not limited to, bacterial and fungal infections, viral infections (including HIV/AIDS and hepatitis), parasitological diseases, tuberculosis and other mycobacterial diseases, vaccinations and other interventions, and drug-resistance, chronic infections, epidemiology and tropical, emergent, pediatric, dermal and sexually-transmitted diseases.
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