Radiology AI in Routine Practice

Real-world deployment reveals adoption gaps and varying engagement with regulator-approved artificial intelligence decision support tools

By
Conexiant News Staff
February 17, 2026
2 min

Conexiant

Objective:

To examine the real-world impact of an AI decision support tool on radiology workflow in a clinical setting.

Approach:

Key Findings:

The AI tool had potential benefits in high workload situations but showed variable engagement among users.
Barriers to sustained engagement included information overload, inconsistent performance, and uncertainty about medicolegal liability.
Implementation challenges persisted despite regulatory approval and technical validation.

Interpretation:

The study indicates that the integration of AI in radiology is complex and requires ongoing evaluation and adaptation to be effective.

Limitations:

Engagement with the AI tool was not uniform across all users and clinical contexts.
The study relied on qualitative data, which may not capture all quantitative impacts.

Conclusion:

Successful implementation of AI in radiology requires clear communication of system limitations, governance structures, and continuous engagement from radiologists.