Accepted for/Published in: JMIR AI
Date Submitted: May 12, 2024
Open Peer Review Period: Jun 3, 2024 - Jul 29, 2024
Date Accepted: Oct 12, 2025
(closed for review but you can still tweet)
Explaining the Slow Adoption of AI Innovations in Healthcare: A Network Analysis Approach
ABSTRACT
Background:
Artificial intelligence (AI) is a topic of considerable hype, with many actors sensing its high potential for healthcare applications. Despite this, the uptake has been slow, with few applications being implemented in clinical practice.
Objective:
A qualitative case study with a mixed method approach was conducted at one of Sweden’s largest hospitals. A literature review and mapping of CE-approved AI medical devices was conducted and primary qualitative data from 14 expert interviews were collected and cross-referenced with secondary quantitative data. The framework technological innovation systems (TIS) was used to analyze the system factors and its dynamics to identify blocking mechanisms and areas for improvement.
Methods:
The present study employed a mixed methods approach that triangulated quantitative and qualitative data to provide a more comprehensive and nuanced understanding of the research topic [34].
Results:
The challenges related to resource mobilization and market formation block the continued development of the innovation system. Establishing a technical infrastructure for testing and validation could strengthen subsequently contribute to reinforcing the entire system, given how functions interact and influence each other.
Conclusions:
As shown by this analysis the adoption of AI healthcare technology innovations can be enhanced by establishing avenues for testing and validation to yield illustrative use cases. Interconnectedness between the guidance of search and entrepreneurial experimentation in the early development of TIS have been confirmed. Additionally, our study provided evidence that TIS undergo innovation cycles, where interactions across the supply and demand side are needed. Clinical Trial: N/A
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.