Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow Efficiency.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Alberto I Pérez-Sanpablo, Jimena Quinzaños-Fresnedo, Josefina Gutiérrez-Martínez, Irma G Lozano-Rodríguez, Ernesto Roldan-Valadez
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引用次数: 0

Abstract

Introduction: To examine the integration of artificial intelligence (AI) into Picture Archiving and Communication Systems (PACS) and assess its impact on medical imaging, diagnostic workflows, and patient outcomes. This review explores the technological evolution, key advancements, and challenges associated with AI-enhanced PACS in healthcare settings.

Methods: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science databases, covering articles from January 2000 to October 2024. Search terms included "artificial intelligence," "machine learning," "deep learning," and "PACS," combined with keywords related to diagnostic accuracy and workflow optimization. Articles were selected based on predefined inclusion and exclusion criteria, focusing on peerreviewed studies that discussed AI applications in PACS, innovations in medical imaging, and workflow improvements. A total of 183 studies met the inclusion criteria, comprising original research, systematic reviews, and meta-analyses.

Results: AI integration in PACS has significantly enhanced diagnostic accuracy, achieving improvements of up to 93.2% in some imaging modalities, such as early tumor detection and anomaly identification. Workflow efficiency has been transformed, with diagnostic times reduced by up to 90% for critical conditions like intracranial hemorrhages. Convolutional neural networks (CNNs) have demonstrated exceptional performance in image segmentation, achieving up to 94% accuracy, and in motion artifact correction, further enhancing diagnostic precision. Natural language processing (NLP) tools have expedited radiology workflows, reducing reporting times by 30-50% and improving consistency in report generation. Cloudbased solutions have also improved accessibility, enabling real-time collaboration and remote diagnostics. However, challenges in data privacy, regulatory compliance, and interoperability persist, emphasizing the need for standardized frameworks and robust security protocols. Conclusions The integration of AI into PACS represents a pivotal transformation in medical imaging, offering improved diagnostic workflows and potential for personalized patient care. Addressing existing challenges and enhancing interoperability will be essential for maximizing the benefits of AIpowered PACS in healthcare.

改变医学影像:人工智能集成在PACS中的作用,以提高诊断准确性和工作流程效率。
简介:研究人工智能(AI)与图像存档和通信系统(PACS)的集成,并评估其对医学成像、诊断工作流程和患者预后的影响。这篇综述探讨了医疗环境中与人工智能增强型PACS相关的技术演变、关键进展和挑战。方法:综合检索PubMed、Scopus和Web of Science数据库,检索时间为2000年1月至2024年10月。搜索词包括“人工智能”、“机器学习”、“深度学习”和“PACS”,以及与诊断准确性和工作流程优化相关的关键词。文章根据预定义的纳入和排除标准进行选择,重点关注同行评议的研究,这些研究讨论了人工智能在PACS中的应用、医学成像的创新和工作流程的改进。共有183项研究符合纳入标准,包括原始研究、系统评价和荟萃分析。结果:AI在PACS中的集成显著提高了诊断准确性,在一些成像方式上,如早期肿瘤检测和异常识别,提高了高达93.2%。工作流程效率发生了转变,对颅内出血等危重情况的诊断时间最多减少了90%。卷积神经网络(cnn)在图像分割方面表现出色,准确率高达94%,在运动伪影校正方面也表现出色,进一步提高了诊断精度。自然语言处理(NLP)工具加快了放射学工作流程,将报告时间缩短了30-50%,并提高了报告生成的一致性。基于云的解决方案也提高了可访问性,支持实时协作和远程诊断。然而,数据隐私、法规遵从性和互操作性方面的挑战仍然存在,这强调了对标准化框架和健壮的安全协议的需求。人工智能与PACS的集成代表了医学成像的关键转变,提供了改进的诊断工作流程和个性化患者护理的潜力。解决现有挑战和增强互操作性对于最大限度地发挥医疗保健领域ai驱动PACS的优势至关重要。
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
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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