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基于云的通用医学图像存储库在大型公共卫生系统中的初步部署:一项方案研究。

Pilot deployment of a cloud-based universal medical image repository in a large public health system: A protocol study.

机构信息

Hospital Israelita Albert Einstein, Sao Paulo, Brazil.

Technology Research and Development Center (GCITE), Federal Institute of Goias, Goiania, Goias, Brazil.

出版信息

PLoS One. 2024 Aug 29;19(8):e0307022. doi: 10.1371/journal.pone.0307022. eCollection 2024.

Abstract

This paper outlines the protocol for the deployment of a cloud-based universal medical image repository system. The proposal aims not only at the deployment but also at the automatic expansion of the platform, incorporating Artificial Intelligence (AI) for the analysis of medical image examinations. The methodology encompasses efficient data management through a universal database, along with the deployment of various AI models designed to assist in diagnostic decision-making. By presenting this protocol, the goal is to overcome technical challenges and issues that impact all phases of the workflow, from data management to the deployment of AI models in the healthcare sector. These challenges include ethical considerations, compliance with legal regulations, establishing user trust, and ensuring data security. The system has been deployed, with a tested and validated proof of concept, possessing the capability to receive thousands of images daily and to sustain the ongoing deployment of new AI models to expedite the analysis process in medical image exams.

摘要

本文概述了部署基于云的通用医学图像存储库系统的方案。该方案不仅旨在部署,还旨在通过人工智能(AI)自动扩展平台,以分析医学图像检查。该方法通过通用数据库实现有效的数据管理,同时部署各种旨在协助诊断决策的 AI 模型。通过提出该方案,旨在克服影响工作流程各个阶段的技术挑战和问题,从数据管理到医疗保健领域 AI 模型的部署。这些挑战包括伦理考虑、法律法规的合规性、建立用户信任和确保数据安全。该系统已经部署,经过测试和验证的概念验证,具有每天接收数千张图像的能力,并能够持续部署新的 AI 模型,以加快医学图像检查的分析过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75b9/11361589/9340313b982c/pone.0307022.g001.jpg

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