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[人工智能对肿瘤学临床实践演变的影响:聚焦语言模型]

[Impact of artificial intelligence on the evolution of clinical practices in oncology: Focus on language models].

作者信息

Morel Daphné, Verlingue Loïc

机构信息

Département d'onco-radiothérapie, Gustave-Roussy, Villejuif, France; Inserm U1030, radiothérapie moléculaire, Villejuif, France.

Centre Léon-Bérard, Centre de recherche en cancérologie de Lyon, Lyon, France.

出版信息

Bull Cancer. 2025 Jan;112(1):54-60. doi: 10.1016/j.bulcan.2024.12.005. Epub 2024 Dec 18.

DOI:10.1016/j.bulcan.2024.12.005
PMID:39701885
Abstract

Artificial intelligence (AI) is addressing many expectations for healthcare practitioners and patients in oncology. It has the potential to deeply transform medical practices as we know them today: improving early diagnosis by analysing large quantities of medical data, refining personalised treatment plans and optimising patient follow-up. AI also makes it easier to identify new biomarkers and predict responses to therapies, reducing margins of error and speeding up clinical decisions. Among the most popular types of AI to revolutionise clinical practice are language models. In a perfect world, the integration of AI would promote more precise, personalised and efficient care, while relieving healthcare providers of tedious or repetitive tasks, allowing them to concentrate more on providing human support to patients, and all this with a low energy consumption. However, the large-scale deployment of AI currently raises fundamental questions about fairness, safety of use and how to assess the results obtained from AI longitudinally. This article explores how the many applications are evaluated for our practice (spoiler alert: they are currently limited), potential clinical benefits and challenges currently encountered when dealing with the integration of AI into routine oncology care. We will focus on language models whose development has been exploding since 2021.

摘要

人工智能(AI)正在满足肿瘤学领域医疗从业者和患者的诸多期望。它有可能深刻改变我们如今所知的医疗实践:通过分析大量医疗数据改善早期诊断、完善个性化治疗方案并优化患者随访。人工智能还使识别新的生物标志物和预测治疗反应变得更加容易,减少误差范围并加快临床决策。语言模型是彻底改变临床实践的最流行的人工智能类型之一。在理想情况下,人工智能的整合将促进更精确、个性化和高效的护理,同时减轻医疗服务提供者繁琐或重复的任务,使他们能够更专注于为患者提供人文支持,而且所有这些都能以低能耗实现。然而,目前人工智能的大规模部署引发了关于公平性、使用安全性以及如何纵向评估从人工智能获得的结果等基本问题。本文探讨了如何对我们实践中的众多应用进行评估(剧透预警:目前这些评估有限)、潜在的临床益处以及在将人工智能整合到常规肿瘤护理中时目前遇到的挑战。我们将重点关注自2021年以来发展迅猛的语言模型。

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