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Image Quality in the Era of Artificial Intelligence: Understanding the Limitations of AI-based Image Reconstruction
Jana G Delfino1, Jason L Granstedt1, Frank W Samuelson1
1Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10993 New Hampshire Ave, Bldg 62-3116, Silver Spring, MD 20993.
Abstract:
Artificial intelligence (AI) is being deployed in radiology for image reconstruction and postprocessing to produce images that appear sharper, smoother, and more detailed. However, incorporating AI also introduces new failure modes and can exacerbate the disconnect between the perceived quality of an image and its diagnostic information content. Understanding the limitations of AI-enabled image reconstruction and postprocessing is essential for the safe and effective use of this technology. Therefore, the purpose of this report is to raise awareness of the limitations of AI-based image reconstruction and postprocessing to help users realize the benefits of the technology while minimizing risks. Accordingly, this report reviews approaches to image quality assessment, discusses the regulatory framework relevant to AI-enabled imaging devices, describes AI-specific failure modes, and outlines strategies to mitigate associated risks. Keywords: Artificial Intelligence, Image Quality Assessment, Hallucinations, Artifacts, Interpretation, Image Reconstruction, Denoising, Superresolution, Postacquisition Processing, Perception, Reconstruction Algorithms, Image Postprocessing © RSNA, 2026.