Medical imaging AI studies emphasized performance more than clinicians' experience
A review identified workflow fit as an implementation concern, while finding limited direct reporting of what helped or hindered artificial intelligence adoption in imaging.
Based on the published abstract. The full paper may contain additional methods, results and limitations.
The 30-second takeaway
Evaluating artificial intelligence and fitting it into everyday imaging work were recurring implementation concerns. Some themes appeared as both helpful and obstructive, and their prominence varied across adoption stages. However, studies emphasized performance metrics more than clinicians' experiences or outcomes, limiting what the review can establish about successful integration into clinical practice.
Most included studies did not directly report implementation barriers and facilitators. This abstract-based account also provides no detailed estimates connecting particular implementation factors with clinicians' outcomes or experiences.
The review examined adoption processes
Researchers synthesized reported factors that helped or hindered the introduction of artificial intelligence into routine imaging. They examined relationships across implementation stages, so the question concerned how tools entered clinical work rather than a treatment comparison.
The same theme could help or hinder
Some themes appeared in reports as both facilitators and barriers. This makes the setting and stage relevant to interpretation, but does not establish that any particular feature will consistently improve adoption or clinician outcomes.
The original publication
Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.
Wenderott K, Krups J, Weigl M et al.
J Med Internet Res · 2025
- PubMed ID
- 40690758
- Record checked
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