大语言模型在围手术期医学中的应用和未来前景:叙述性回顾。

IF 3.4 3区 医学 Q1 ANESTHESIOLOGY
Arnaud Romeo Mbadjeu Hondjeu, Zi Ying Zhao, Luka Newton, Anass Ajenkar, Emily Hladkowicz, Karim Ladha, Duminda N Wijeysundera, Daniel I McIsaac
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引用次数: 0

摘要

目的:大型语言模型(llm)是人工智能(AI)和语言学的一个子集,旨在帮助计算机理解和分析人类语言。法学硕士的临床应用最近被认为具有潜在的增强分析能力。随着时间的推移,法学硕士的可用性和性能预计将大幅提高,对患者护理和医疗保健提供者的工作流程产生重大影响。尽管人们越来越认识到llm,但在围手术期临床医生中,对其效用、相关益处和局限性的见解却很少。在这篇叙述性综述中,我们深入探讨了现有llm的功能和前景及其在围手术期医学中的临床应用。此外,我们总结了必须解决的挑战和限制,以充分发挥法学硕士的潜力。来源:我们检索了MEDLINE、谷歌Scholar和PubMed®数据库中有关llm围手术期护理的文章。主要发现:我们发现,在围手术期(从手术诊断到术后出院),llm有潜力通过提取和汇总临床数据,根据这些发现提出建议,以及解决患者的问题,提高医疗保健服务的效率和准确性。此外,法学硕士可以用于临床决策支持,监测工具,预测建模,并加强医学研究和教育。结论:llm与围手术期医学的整合为提高患者护理、临床决策和操作效率提供了重要机会。这些模型可以简化流程,提供个性化的患者教育,并提供强大的决策支持。然而,它们的临床应用需要解决几个关键挑战,包括管理幻觉、确保数据安全以及减轻固有偏见。如果这些挑战得到满足,法学硕士可以彻底改变围手术期实践,改善患者的治疗效果和临床医生的工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large language models in perioperative medicine-applications and future prospects: a narrative review.

Purpose: Large language models (LLMs) are a subset of artificial intelligence (AI) and linguistics designed to help computers understand and analyze human language. Clinical applications of LLMs have recently been recognised for their potential enhanced analytic capacity. Availability and performance of LLMs are expected to increase substantially over time with a significant impact on patient care and health care provider workflow. Despite increasing recognition of LLMs, insights on the utilities, associated benefits and limitations are scarce among perioperative clinicians. In this narrative review, we delve into the functionalities and prospects of existing LLMs and their clinical application in perioperative medicine. Furthermore, we summarize challenges and constraints that must be addressed to fully realize the potential of LLMs.

Source: We searched MEDLINE, Google Scholar, and PubMed® databases for articles referencing LLMs in perioperative care.

Principal findings: We found that in the perioperative setting (from surgical diagnosis to discharge postoperatively), LLMs have the potential to improve the efficiency and accuracy of health care delivery by extracting and summarizing clinical data, making recommendations on the basis of these findings, as well as addressing patient queries. Moreover, LLMs can be used for clinical decision-making support, surveillance tools, predictive modelling, and enhancement of medical research and education.

Conclusions: The integration of LLMs into perioperative medicine presents a significant opportunity to enhance patient care, clinical decision-making, and operational efficiency. These models can streamline processes, provide personalized patient education, and offer robust decision support. Nevertheless, their clinical implementation requires addressing several key challenges, including managing hallucinations, ensuring data security, and mitigating inherent biases. If these challenges are met, LLMs can revolutionize perioperative practice, improving both patient outcomes and clinician workflow.

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来源期刊
CiteScore
8.50
自引率
7.10%
发文量
161
审稿时长
6-12 weeks
期刊介绍: The Canadian Journal of Anesthesia (the Journal) is owned by the Canadian Anesthesiologists’ Society and is published by Springer Science + Business Media, LLM (New York). From the first year of publication in 1954, the international exposure of the Journal has broadened considerably, with articles now received from over 50 countries. The Journal is published monthly, and has an impact Factor (mean journal citation frequency) of 2.127 (in 2012). Article types consist of invited editorials, reports of original investigations (clinical and basic sciences articles), case reports/case series, review articles, systematic reviews, accredited continuing professional development (CPD) modules, and Letters to the Editor. The editorial content, according to the mission statement, spans the fields of anesthesia, acute and chronic pain, perioperative medicine and critical care. In addition, the Journal publishes practice guidelines and standards articles relevant to clinicians. Articles are published either in English or in French, according to the language of submission.
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