Responsible Artificial Intelligence Off-Boarding in Radiology: Staff Perspectives on Decommissioning and a Proposed Withdrawal Framework

IF 5.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology Pub Date : 2026-10-01 Epub Date: 2026-06-06 DOI:10.1016/j.jacr.2026.06.004

Jack Packer MSc, Geraldine Dean MD, MSc, FRCR, Mathew Storey FRCR, Christina Malamateniou FCR, PhD, Susan Cheng Shelmerdine MBBS, FRCR, PhD

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引用次数: 0

Abstract

Purpose

Artificial intelligence (AI) governance commonly emphasizes procurement, validation, deployment, and performance monitoring but lack guidance on how embedded AI tools should be withdrawn when funding, contracts, or strategy change abruptly. We examined staff experience after withdrawal of a chest radiograph AI triage tool and developed a practical framework for responsible AI off-boarding.

Methods

An anonymous staff survey was circulated 2 months after decommissioning of a chest radiograph AI triage tool at a multisite National Health Service trust. The survey repeated selected items from three earlier implementation-phase surveys, with added decommissioning-specific questions on workflow, perceived patient benefit, emotional burden, and future AI engagement. Quantitative responses were summarized descriptively. Free text responses were analyzed deductively and interpreted using the Job Demands-Resources model.

Results

The response rate was 21.4% (40 of 187), comparable to earlier survey rounds. Perceived patient benefit from AI remained stable, with 70% (28 of 40) agreement postdecommissioning versus 71.1% (32 of 45) pre-implementation, 65.5% (19 of 29) early implementation, and 67.9% (36 of 53) late implementation. Perceived logistical burden postdecommissioning (35%, 14 of 40) was higher than at late implementation (26.4%, 14 of 53) but lower than at early implementation (51.7%, 15 of 29). Response rate from reporting staff was low (5 of 40), with two voicing disappointment in AI tool withdrawal, and one stating they had grown reliant on the tool. Across 22 free text entries, frequent themes included loss of clinical value or pathway efficiency, operational relief after withdrawal, and patient-facing emotional labor during AI-enabled escalation.

Conclusions

Decommissioning produces both operational relief and perceived clinical loss, with effects differing by staff role. We propose a three-phase AI Off-Boarding Protocol: prewithdrawal assessment, graduated transition and postwithdrawal support. Decommissioning management should be treated as a central part of responsible governance across the AI life cycle.

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放射科负责任的人工智能离职：员工对退役和拟议退出框架的看法。

目的：人工智能治理通常强调采购、验证、部署和绩效监控，但缺乏在资金、合同或战略突然变化时如何撤回嵌入式人工智能工具的指导。我们检查了撤下胸片人工智能分诊工具后的工作人员经验，并制定了负责任的人工智能离职的实用框架。方法：在一个多站点NHS信托机构停用胸片人工智能分诊工具两个月后，进行了一项匿名员工调查。该调查重复了三个早期实施阶段调查的选定项目，并增加了有关工作流程、感知患者利益、情感负担和未来人工智能参与的退役特定问题。定量反应进行描述性总结。使用工作需求-资源（Job requirements - resources, JD-R）模型对自由文本回答进行演绎分析和解释。结果：回复率为21.4%(40/187)，与前几轮调查相当。人工智能的患者获益保持稳定，退役后70%（28/40）同意，实施前71.1%(32/45)，早期实施65.5%(19/29)，晚期实施67.9%（36/53）。退役后的后勤负担（35%,14/40）高于实施后期（26.4%,14/53），但低于实施早期（51.7%,15/29）。报告人员的回复率很低（5/40），其中两人表示对人工智能工具的退出感到失望，一人表示他们已经越来越依赖该工具。在22个自由文本条目中，常见的主题包括临床价值或通路效率的丧失，停药后的操作缓解，以及人工智能升级期间患者面对的情绪劳动。结论：退役既可以减轻操作负担，也可以减轻临床损失，其效果因工作人员角色而异。我们提出了一个分三个阶段的人工智能离职协议——撤军前评估、逐步过渡和撤军后支持。退役管理应被视为人工智能生命周期中负责任治理的核心部分。

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

Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

6.30

自引率

8.90%

发文量

312

审稿时长

34 days

期刊介绍： The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.