优化癌症成像的人工智能:用户体验研究。

IF 3.3 Q2 ONCOLOGY
JMIR Cancer Pub Date : 2024-10-10 DOI:10.2196/52639
Iman Hesso, Lithin Zacharias, Reem Kayyali, Andreas Charalambous, Maria Lavdaniti, Evangelia Stalika, Tarek Ajami, Wanda Acampa, Jasmina Boban, Shereen Nabhani-Gebara
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引用次数: 0

摘要

背景:提高临床疗效和效率的需求一直是医学成像领域开发人工智能(AI)工具的主要动力。INCISIVE 项目是一项由欧盟资助的计划,旨在利用人工智能技术彻底改变癌症成像方法。该项目旨在通过开发基于人工智能的工具箱,提高准确性、特异性、灵敏度、可解释性和成本效益,从而解决成像技术的局限性:为确保 INCISIVE 人工智能服务的成功实施,我们开展了一项研究,以了解医护专业人员(HCPs)对拟议工具箱的需求、挑战和期望,以及任何潜在的实施障碍:方法:开展了一项包含两个阶段的混合方法研究。第 1 阶段包括与 INCISIVE AI 工具箱用户进行用户体验(UX)设计研讨会。第二阶段是通过一系列连续问卷进行德尔菲研究。在人员招募方面,采用了基于项目联盟网络的目的性抽样策略。共有 16 名来自塞尔维亚、意大利、希腊、塞浦路斯、西班牙和英国的高级保健人员参加了用户体验设计研讨会,12 人完成了德尔菲研究。使用 SPSS(IBM 公司)进行了描述性统计,从而计算出德尔菲研究清单的平均等级分。通过用户体验设计研讨会收集的定性数据使用 NVivo(第 12 版;Lumivero)软件进行了分析:研讨会促进了头脑风暴,并确定了 INCISIVE AI 工具箱的理想功能和实施障碍。随后,德尔菲研究有助于对这些功能进行排序,显示出 HCP 之间的强烈共识(W=0.741,PConclusions):研究结果全面考察了 INCISIVE 人工智能工具箱的设计要素,包括最终用户的要求和潜在的实施障碍,从而为 INCISIVE 技术的设计和实施提供了指导。研究结果提供了有关 INCISIVE 人工智能工具箱在以下三项服务中所需的人工智能可解释性程度的信息:(1)初步诊断;(2)疾病分期、分化和特征描述;以及(3)工具箱指明的治疗和随访。通过考虑最终用户的观点,INCISIVE 旨在开发一种能有效满足其需求并推动其采用的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence for Optimizing Cancer Imaging: User Experience Study.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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