Pharmacy Department, Faculty of Health, Science, Social Care and Education, Kingston University London, Kingston Upon Thames, United Kingdom.
Department of Nursing, Cyprus University of Technology, Limassol, Cyprus.
JMIR Cancer. 2024 Oct 10;10:e52639. doi: 10.2196/52639.
The need for increased clinical efficacy and efficiency has been the main force in developing artificial intelligence (AI) tools in medical imaging. The INCISIVE project is a European Union-funded initiative aiming to revolutionize cancer imaging methods using AI technology. It seeks to address limitations in imaging techniques by developing an AI-based toolbox that improves accuracy, specificity, sensitivity, interpretability, and cost-effectiveness.
To ensure the successful implementation of the INCISIVE AI service, a study was conducted to understand the needs, challenges, and expectations of health care professionals (HCPs) regarding the proposed toolbox and any potential implementation barriers.
A mixed methods study consisting of 2 phases was conducted. Phase 1 involved user experience (UX) design workshops with users of the INCISIVE AI toolbox. Phase 2 involved a Delphi study conducted through a series of sequential questionnaires. To recruit, a purposive sampling strategy based on the project's consortium network was used. In total, 16 HCPs from Serbia, Italy, Greece, Cyprus, Spain, and the United Kingdom participated in the UX design workshops and 12 completed the Delphi study. Descriptive statistics were performed using SPSS (IBM Corp), enabling the calculation of mean rank scores of the Delphi study's lists. The qualitative data collected via the UX design workshops was analyzed using NVivo (version 12; Lumivero) software.
The workshops facilitated brainstorming and identification of the INCISIVE AI toolbox's desired features and implementation barriers. Subsequently, the Delphi study was instrumental in ranking these features, showing a strong consensus among HCPs (W=0.741, P<.001). Additionally, this study also identified implementation barriers, revealing a strong consensus among HCPs (W=0.705, P<.001). Key findings indicated that the INCISIVE AI toolbox could assist in areas such as misdiagnosis, overdiagnosis, delays in diagnosis, detection of minor lesions, decision-making in disagreement, treatment allocation, disease prognosis, prediction, treatment response prediction, and care integration throughout the patient journey. Limited resources, lack of organizational and managerial support, and data entry variability were some of the identified barriers. HCPs also had an explicit interest in AI explainability, desiring feature relevance explanations or a combination of feature relevance and visual explanations within the toolbox.
The results provide a thorough examination of the INCISIVE AI toolbox's design elements as required by the end users and potential barriers to its implementation, thus guiding the design and implementation of the INCISIVE technology. The outcome offers information about the degree of AI explainability required of the INCISIVE AI toolbox across the three services: (1) initial diagnosis; (2) disease staging, differentiation, and characterization; and (3) treatment and follow-up indicated for the toolbox. By considering the perspective of end users, INCISIVE aims to develop a solution that effectively meets their needs and drives adoption.
提高临床疗效和效率的需求一直是在医学成像中开发人工智能 (AI) 工具的主要动力。INCISIVE 项目是一个欧盟资助的项目，旨在利用 AI 技术彻底改变癌症成像方法。它旨在通过开发一个基于 AI 的工具包来解决成像技术的局限性，该工具包可以提高准确性、特异性、灵敏度、可解释性和成本效益。
为了确保 INCISIVE AI 服务的成功实施，进行了一项研究，以了解医疗保健专业人员 (HCP) 对拟议工具包的需求、挑战和期望，以及任何潜在的实施障碍。
采用混合方法研究，包括 2 个阶段。第 1 阶段涉及使用 INCISIVE AI 工具包的用户进行用户体验 (UX) 设计研讨会。第 2 阶段通过一系列顺序问卷进行了 Delphi 研究。为了招募参与者，采用了基于项目联盟网络的目的抽样策略。共有来自塞尔维亚、意大利、希腊、塞浦路斯、西班牙和英国的 16 名 HCP 参加了 UX 设计研讨会，其中 12 名完成了 Delphi 研究。使用 SPSS（IBM Corp）进行描述性统计，能够计算 Delphi 研究列表的平均等级分数。通过 NVivo（版本 12；Lumivero）软件分析通过 UX 设计研讨会收集的定性数据。
研讨会促进了头脑风暴，并确定了 INCISIVE AI 工具包的所需功能和实施障碍。随后，Delphi 研究在对这些功能进行排名方面发挥了重要作用，HCP 之间表现出强烈的共识（W=0.741，P<.001）。此外，这项研究还确定了实施障碍，HCP 之间表现出强烈的共识（W=0.705，P<.001）。主要发现表明，INCISIVE AI 工具包可以帮助解决误诊、过度诊断、诊断延迟、小病变检测、意见不合时的决策、治疗分配、疾病预后、预测、治疗反应预测以及整个患者治疗过程中的治疗整合。资源有限、缺乏组织和管理支持以及数据输入变异性是一些已确定的障碍。HCP 还明确表示对 AI 可解释性感兴趣，希望在工具包中提供特征相关性解释或特征相关性和可视化解释的组合。
结果对 INCISIVE AI 工具包的设计要素进行了全面检查，这些要素是最终用户所要求的，并且对其实施的潜在障碍进行了检查，从而指导了 INCISIVE 技术的设计和实施。结果提供了有关 INCISIVE AI 工具包在三个服务中的 AI 可解释性要求的信息：(1) 初始诊断；(2) 疾病分期、分化和特征描述；(3) 工具包指示的治疗和随访。通过考虑最终用户的角度，INCISIVE 旨在开发一种能够有效满足他们需求并推动采用的解决方案。
