Relevance of advanced imaging analysis units in radiology departments: a narrative review.

IF 2.3 3区医学 Q3 ENGINEERING, BIOMEDICAL

International Journal of Computer Assisted Radiology and Surgery Pub Date : 2025-10-04 DOI:10.1007/s11548-025-03529-4

Teodoro Martín-Noguerol, Félix Paulano-Godino, Pilar López-Úbeda, Roy F Riascos, Antonio Luna

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引用次数: 0

Abstract

Purpose: Radiology departments (RDs) face an increasing volume of data, images, and information, leading to a higher workload for radiologists. The integration of artificial intelligence (AI) presents an opportunity to optimize workflows and reduce the burden on radiologists. This review explores the role of advanced imaging analysis units (AIAUs) in enhancing radiological processes and improving overall patient outcomes.

Methods: A literature review was conducted to assess the impact of AI-driven AIAUs on RD workflows. The study examines the collaboration between radiologists, technicians, and biomedical engineers in the extraction and processing of imaging data. Additionally, the integration of AI algorithms for task automation is analyzed.

Results: The implementation of AIAUs in RDs has the potential to enhance workflow efficiency by minimizing radiologists' workload and improving imaging analysis. These units facilitate collaborative work among radiologists, technicians, and engineers, fostering continuous communication, feedback, and training. AI algorithms incorporated into AIAUs support automation, streamlining pre- and postprocessing imaging tasks.

Conclusion: AIAUs represent a promising approach to optimizing RD workflows and improving patient outcomes. Their successful implementation requires a multidisciplinary approach, integrating AI technologies with the expertise of radiologists, technicians, and biomedical engineers. Continuous collaboration and education within these units will be essential to maximize the benefits of emerging digital technologies in radiology.

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放射科先进影像分析单元的相关性：叙述性回顾。

目的：放射科（rd）面临着越来越多的数据、图像和信息，导致放射科医生的工作量增加。人工智能（AI）的集成为优化工作流程和减轻放射科医生的负担提供了机会。这篇综述探讨了先进成像分析单元（aiau）在增强放射学过程和改善患者整体预后方面的作用。方法：通过文献综述来评估人工智能驱动的aiau对研发工作流程的影响。该研究考察了放射科医生、技术人员和生物医学工程师在提取和处理成像数据方面的合作。此外，还分析了任务自动化中人工智能算法的集成。结果：在rd中实施aiau有可能通过减少放射科医生的工作量和改善成像分析来提高工作效率。这些单元促进了放射科医生、技术人员和工程师之间的协作工作，促进了持续的沟通、反馈和培训。人工智能算法集成到aiau中，支持自动化，简化预处理和后处理成像任务。结论：aiau是一种很有前景的优化RD工作流程和改善患者预后的方法。它们的成功实施需要多学科方法，将人工智能技术与放射科医生、技术人员和生物医学工程师的专业知识相结合。这些单位之间的持续合作和教育对于最大限度地发挥放射学新兴数字技术的效益至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

CiteScore

5.90

自引率

6.70%

发文量

243

审稿时长

6-12 weeks

期刊介绍： The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.