The Potential of Generative AI in Radiology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 44

Special Issue Editors


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Guest Editor
Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
Interests: generative AI; foundation deep learning models

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Guest Editor
Department of Radiology, Emory University School of Medicine, Atlanta, GA, USA
Interests: ethical principles in using AI; model bias; foundation deep learning models
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Special Issue Information

Dear Colleagues,

The field of radiology is on the verge of a revolutionary transformation, driven by the rapid advancements in generative artificial intelligence (AI). This Special Issue of Diagnostics focuses on exploring the potential applications and implications of cutting-edge image generation algorithms and large language models (LLMs) in the context of radiological practice and research.

As AI continues to evolve, it opens up new possibilities for enhancing diagnostic accuracy, streamlining workflows, and improving patient outcomes. From generating synthetic medical images for training purposes to assisting in image interpretation and report generation, generative AI holds immense promise in reshaping the landscape of radiology.

This Special Issue invites original research articles, review papers, and case studies that explore various aspects of employing generative AI techniques in radiology. We encourage submissions that address the technical challenges, ethical considerations, and clinical impact of integrating these powerful tools into radiological practice. By bringing together experts from the fields of radiology, computer science, and AI, we aim to foster interdisciplinary collaborations and drive innovation in this rapidly evolving domain.

Through this Special Issue, we seek to provide a comprehensive overview of the current state of the art in generative AI for radiology and chart the future directions for research and clinical implementation.

Dr. Bardia Khosravi
Dr. Judy Wawira Gichoya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative AI
  • large language models
  • radiology
  • privacy preservation
  • bias

Published Papers

This special issue is now open for submission.
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