A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models.

Journal: International journal of medical informatics

PMID: 40179622

Abstract

BACKGROUND: Medical imaging is a vital diagnostic tool that provides detailed insights into human anatomy but faces challenges affecting its accuracy and efficiency. Advanced generative AI models offer promising solutions. Unlike previous reviews with a narrow focus, a comprehensive evaluation across techniques and modalities is necessary.

Authors

Chaimaa Oulmalme

University of Quebec at Chicoutimi, Quebec, Canada. Electronic address: coulmalme@etu.uqac.ca.
Haïfa Nakouri

University of Quebec at Chicoutimi, Quebec, Canada; Université de Tunis, LARODEC, ISG Tunis, Tunisia. Electronic address: hnakouri@uqac.ca.
Fehmi Jaafar

University of Quebec at Chicoutimi, Quebec, Canada. Electronic address: fjaafar@uqac.ca.

Keywords

Algorithms Artificial Intelligence Diagnostic Imaging Humans Image Enhancement Image Processing, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (40179622)

A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models.

Abstract

Authors

Keywords

External Resources

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