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Artificial Intelligence for Optimizing Cancer Imaging: User Experience Study.

Iman Hesso¹, Lithin Zacharias¹, Reem Kayyali¹

¹Pharmacy Department, Faculty of Health, Science, Social Care and Education, Kingston University London, Kingston Upon Thames, United Kingdom.

|October 10, 2024

Summary

Artificial intelligence (AI) in medical imaging can improve cancer diagnosis and treatment. The INCISIVE project developed an AI toolbox, gathering healthcare professional feedback on features and implementation barriers for better clinical integration.

Keywords:

Delphi method INCISIVE AI toolbox UX design workshops artificial intelligence cancer cancer imaging user experience

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

The INCISIVE project, an EU initiative, aims to enhance cancer imaging using AI.
The project focuses on developing an AI toolbox to improve diagnostic accuracy, specificity, sensitivity, interpretability, and cost-effectiveness.

Purpose of the Study:

To understand healthcare professionals' (HCPs) needs, challenges, and expectations for the INCISIVE AI toolbox.
To identify potential barriers to the implementation of AI in medical imaging.

Main Methods:

A mixed-methods study involving user experience (UX) design workshops and a two-phase Delphi study.
Recruitment utilized a purposive sampling strategy within the INCISIVE consortium network.
Data analysis included descriptive statistics (SPSS) and qualitative analysis (NVivo).

Main Results:

Workshops identified desired features and implementation barriers for the AI toolbox.
The Delphi study achieved strong consensus on feature ranking (W=0.741) and barriers (W=0.705).
Key findings highlight AI's potential in diagnosis, staging, treatment response prediction, and care integration, while noting barriers like limited resources and data variability. HCPs emphasized the need for AI explainability.

Conclusions:

The study provides end-user insights into the INCISIVE AI toolbox's design and implementation.
Results guide the development of AI explainability features for diagnosis, staging, and treatment/follow-up services.
Incorporating end-user perspectives aims to ensure the INCISIVE AI solution meets needs and drives adoption in clinical practice.