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Artificial Intelligence for Optimizing Cancer Imaging:User Experience Study
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DOI：10.2196/52639.png)
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Abstract
En 中文
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 cancerimaging methods using AI technology. It seeks to address limitations in imaging techniques by developing an AI-based toolboxthat 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 theneeds, challenges, and expectations of health care professionals (HCPs) regarding the proposed toolbox and any potentialimplementation barriers. Methods: A mixed methods study consisting of 2 phases was conducted. Phase 1 involved user experience (UX) designworkshops with users of the INCISIVE AI toolbox. Phase 2 involved a Delphi study conducted through a series of sequentialquestionnaires. To recruit, a purposive sampling strategy based on the project's consortium network was used. In total, 16 HCPsfrom Serbia, Italy, Greece, Cyprus, Spain, and the United Kingdom participated in the UX design workshops and 12 completedthe Delphi study. Descriptive statistics were performed using SPSS (IBM Corp), enabling the calculation of mean rank scores ofthe 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 andimplementation barriers. Subsequently, the Delphi study was instrumental in ranking these features, showing a strong consensusamong HCPs (W=0.741, P<.001). Additionally, this study also identified implementation barriers, revealing a strong consensusamong 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, diseaseprognosis, prediction, treatment response prediction, and care integration throughout the patient journey. Limited resources, lackof organizational and managerial support, and data entry variability were some of the identified barriers. HCPs also had an explicitinterest in AI explainability, desiring feature relevance explanations or a combination of feature relevance and visual explanationswithin the toolbox. Conclusions: The results provide a thorough examination of the INCISIVE AI toolbox's design elements as required by theend 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 threeservices: (1) initial diagnosis; (2) disease staging, differentiation, and characterization; and (3) treatment and follow-up indicatedfor the toolbox. By considering the perspective of end users, INCISIVE aims to develop a solution that effectively meets theirneeds and drives adoption
Keywords:
artificial intelligence
cancer
cancer imaging
UX design workshops
Delphi method
INCISIVE AI toolbox
user experience
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