A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology
Issued Date
2024-09-01
Resource Type
eISSN
21998531
Scopus ID
2-s2.0-85202059694
Journal Title
Journal of Open Innovation: Technology, Market, and Complexity
Volume
10
Issue
3
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Open Innovation: Technology, Market, and Complexity Vol.10 No.3 (2024)
Suggested Citation
Nirapai A., Leelasantitham A. A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology. Journal of Open Innovation: Technology, Market, and Complexity Vol.10 No.3 (2024). doi:10.1016/j.joitmc.2024.100369 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/100671
Title
A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology
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Author's Affiliation
Corresponding Author(s)
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Abstract
This study introduces a new adoption model for assessing the quality of experience (QoE) of radiologists using AI-based medical imaging technology. While AI has increasingly been used by radiologists for screening, diagnosis, and classification of medical images, previous investigations have primarily focused on metrics such as effectiveness, efficiency, and satisfaction. This research expands the evaluation criteria to include user experience and user interface (UX/UI) factors, integrating them within the broader QoE. QoE is conceptualized as a multifaceted construct influenced by both human and system factors, which affect cognitive perception, including hedonic and pragmatic aspects. Data were collected from 159 hospital radiologists with prior experience in using AI technology in medical imaging systems through a structured questionnaire. The data were then analyzed using structural equation modeling principles. The findings suggest that contextual content, human factors, and system characteristics significantly influence cognitive perception, which in turn affects the adoption and utilization of AI in medical imaging. This adoption model captures the radiologists' integration of AI throughout various stages of radiological procedures, including scheduling, scanning, acquisition, interpretation, reporting, and communication. The study also highlights the importance of data collection, storage, and sharing practices in compliance with privacy policies.