Establishing a framework for development, prioritization, and assessment of artificial intelligence technology within the radiology department at a large UK teaching hospital.

BJR artificial intelligence Pub Date : 2026-05-07 eCollection Date: 2026-01-01 DOI:10.1093/bjrai/ubag011
James H R Cairns, Beverley Riley, Hanif Ismail, Bashar Al-Qaisieh, Mohua Siddique, Christopher Herbert, Bob Wheller, Fahmid U H Chowdhury, Andrew Scarsbrook
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Abstract

Artificial intelligence (AI) can revolutionize clinical workflows in radiology but requires organizational change. An institutional strategy to develop and evaluate AI tools is outlined. A multidisciplinary AI board with a patient and public involvement and engagement group was created. A comprehensive framework was formed, comprising workstreams covering information governance; technical rigor, performance, and safety; economic considerations; and ethical and medical-legal aspects. In addition to recurring meetings, a workshop with clinicians, information technology specialists, and patient representatives helped to identify priority use cases. Technical infrastructure was enhanced to support the development, performance assessment, and deployment of AI tools. Primary areas for AI applications included training staff, vetting of image requests, quality assurance, image interpretation, and communicating imaging findings to patients. Potential barriers, gaps in evidence, and subsequent actions for AI implementation were outlined. Avenues for collaboration with industry and market-available solutions were outlined. A virtual Picture Archiving and Communication System server was developed and then connected to a deployment platform for performance evaluation of AI products. Establishing an institutional AI board and imaging AI sandbox has guided safe, effective AI implementation while creating an ideal setting for innovation and industry partnership. Our approach to the integration of imaging AI provides a pragmatic guide for other institutions.

在英国一家大型教学医院的放射科建立人工智能技术的开发、优先排序和评估框架。
人工智能(AI)可以彻底改变放射学的临床工作流程,但需要组织变革。概述了开发和评估人工智能工具的制度战略。成立了一个多学科人工智能委员会,由患者和公众参与和参与小组组成。形成了一个全面的框架,包括涵盖信息治理的工作流程;技术严谨、性能优良、安全可靠;经济因素;以及伦理和医疗法律方面的问题。除了定期的会议之外,由临床医生、信息技术专家和患者代表组成的研讨会有助于确定优先用例。技术基础设施得到增强,以支持人工智能工具的开发、性能评估和部署。人工智能应用的主要领域包括培训员工、审查图像请求、质量保证、图像解释以及向患者传达成像结果。概述了人工智能实施的潜在障碍、证据差距和后续行动。概述了与工业界合作的途径和市场上可用的解决方案。开发了虚拟图片存档与通信系统服务器,并与人工智能产品性能评估部署平台对接。建立一个机构人工智能委员会和成像人工智能沙盒,指导了安全、有效的人工智能实施,同时为创新和行业合作创造了理想的环境。我们整合成像人工智能的方法为其他机构提供了实用的指导。
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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