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...

Overview

Journal Radiol Med

Specialty Radiology

Date 2024 Oct 13

PMID 39397129

Authors

Antonio Piras

Ilaria Morelli

Riccardo Ray Colciago

Luca Boldrini

Andrea DAviero

Francesca De Felice

Roberta Grassi

Giuseppe Carlo Iorio

Silvia Longo

Federico Mastroleo

Isacco Desideri

Viola Salvestrini

Affiliations

Soon will be listed here.

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.

Citing Articles

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PMID: 39538068 DOI: 10.1007/s11604-024-01702-4.

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