Uncertainty quantification improves reliability of medical imaging generative AI, systematic review finds

Integrating uncertainty quantification (UQ) into generative models was frequently associated with improved performance or more informative reliability assessment in medical imaging. A systematic review of 41 studies found radiology dominated (29 studies) and 56% of studies were…

• Radiology was the most common application domain, with 29 of 41 studies focusing on radiological imaging.
• Key priorities include standardizing UQ metrics, improving computational efficiency, and embedding uncertainty awareness into generation processes.
• The field is growing rapidly: 56% of included studies were published in 2025.

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