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放射肿瘤学家工作中数字辅助的持续改进:从基于网络的列线图到大型语言模型(LLMs)的应用。意大利放射治疗与临床肿瘤学协会(AIRO)青年小组的系统评价

The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO).

作者信息

Piras Antonio, Morelli Ilaria, Colciago Riccardo Ray, Boldrini Luca, D'Aviero Andrea, De Felice Francesca, Grassi Roberta, Iorio Giuseppe Carlo, Longo Silvia, Mastroleo Federico, Desideri Isacco, Salvestrini Viola

机构信息

UO Radioterapia Oncologica, Villa Santa Teresa, 90011, Bagheria, Palermo, Italy.

Ri.Med Foundation, 90133, Palermo, Italy.

出版信息

Radiol Med. 2024 Nov;129(11):1720-1735. doi: 10.1007/s11547-024-01891-y. Epub 2024 Oct 13.

Abstract

PURPOSE

Recently, the availability of online medical resources for radiation oncologists and trainees has significantly expanded, alongside the development of numerous artificial intelligence (AI)-based tools. This review evaluates the impact of web-based clinical decision-making tools in the clinical practice of radiation oncology.

MATERIAL AND METHODS

We searched databases, including PubMed, EMBASE, and Scopus, using keywords related to web-based clinical decision-making tools and radiation oncology, adhering to PRISMA guidelines.

RESULTS

Out of 2161 identified manuscripts, 70 were ultimately included in our study. These papers all supported the evidence that web-based tools can be transversally integrated into multiple radiation oncology fields, with online applications available for dose and clinical calculations, staging and other multipurpose intents. Specifically, the possible benefit of web-based nomograms for educational purposes was investigated in 35 of the evaluated manuscripts. As regards to the applications of digital and AI-based tools to treatment planning, diagnosis, treatment strategy selection and follow-up adoption, a total of 35 articles were selected. More specifically, 19 articles investigated the role of these tools in heterogeneous cancer types, while nine and seven articles were related to breast and head & neck cancers, respectively.

CONCLUSIONS

Our analysis suggests that employing web-based and AI tools offers promising potential to enhance the personalization of cancer treatment.

摘要

目的

近年来,随着众多基于人工智能(AI)工具的发展,放射肿瘤学家和实习生可获取的在线医学资源显著增加。本综述评估了基于网络的临床决策工具在放射肿瘤学临床实践中的影响。

材料与方法

我们按照PRISMA指南,使用与基于网络的临床决策工具和放射肿瘤学相关的关键词,检索了包括PubMed、EMBASE和Scopus在内的数据库。

结果

在2161篇已识别的手稿中,最终有70篇被纳入我们的研究。这些论文均支持以下证据:基于网络的工具可横向整合到多个放射肿瘤学领域,有可用于剂量和临床计算、分期及其他多用途的在线应用程序。具体而言,在35篇评估手稿中研究了基于网络的列线图用于教育目的的可能益处。关于数字和基于AI的工具在治疗计划、诊断、治疗策略选择和随访中的应用,共筛选出35篇文章。更具体地说,19篇文章研究了这些工具在不同癌症类型中的作用,而分别有9篇和7篇文章与乳腺癌和头颈癌相关。

结论

我们的分析表明,使用基于网络的工具和AI有望提高癌症治疗的个性化水平。

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