Image Quality in the Era of Artificial Intelligence
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917263212281856 |
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| author | Delfino, Jana G. Granstedt, Jason L. Samuelson, Frank W. Ochs, Robert Juluru, Krishna |
| author_facet | Delfino, Jana G. Granstedt, Jason L. Samuelson, Frank W. Ochs, Robert Juluru, Krishna |
| contents | Artificial intelligence (AI) is being deployed within radiology at a rapid pace. AI has proven an excellent tool for reconstructing and enhancing images that appear sharper, smoother, and more detailed, can be acquired more quickly, and allowing clinicians to review them more rapidly. However, incorporation of AI also introduces new failure modes and can exacerbate the disconnect between perceived quality of an image and information content of that image. Understanding the limitations of AI-enabled image reconstruction and enhancement is critical for safe and effective use of the technology. Hence, the purpose of this communication is to bring awareness to limitations when AI is used to reconstruct or enhance a radiological image, with the goal of enabling users to reap benefits of the technology while minimizing risks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09347 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Image Quality in the Era of Artificial Intelligence Delfino, Jana G. Granstedt, Jason L. Samuelson, Frank W. Ochs, Robert Juluru, Krishna Artificial Intelligence Artificial intelligence (AI) is being deployed within radiology at a rapid pace. AI has proven an excellent tool for reconstructing and enhancing images that appear sharper, smoother, and more detailed, can be acquired more quickly, and allowing clinicians to review them more rapidly. However, incorporation of AI also introduces new failure modes and can exacerbate the disconnect between perceived quality of an image and information content of that image. Understanding the limitations of AI-enabled image reconstruction and enhancement is critical for safe and effective use of the technology. Hence, the purpose of this communication is to bring awareness to limitations when AI is used to reconstruct or enhance a radiological image, with the goal of enabling users to reap benefits of the technology while minimizing risks. |
| title | Image Quality in the Era of Artificial Intelligence |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.09347 |