Call for papers - AI in radiology: revolutionizing medical imaging and interpretation

Guest Editors

Salvatore Claudio Fanni, MD, University of Pisa, Italy
Jingyu Zhong, MD, Shanghai Jiao Tong University School of Medicine, China

Submission Status: Open | Submission Deadline: 4 April 2025

BMC Artificial Intelligence is calling for submissions to our Collection, AI in radiology: revolutionizing medical imaging and interpretation. This Collection gathers innovative research on the integration of AI in radiology, focusing on its transformative impact on medical imaging, diagnosis, and healthcare delivery. We invite submissions that explore the application of AI algorithms, machine learning, and automated image interpretation in radiology, with the aim of advancing clinical practice and improving patient outcomes.

New Content Item This Collection supports and amplifies research related to SDG 3: Good Health & Well-Being, SDG 9: Industry, Innovation & Infrastructure.

Meet the Guest Editors

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Salvatore Claudio Fanni, MD, University of Pisa, Italy

Salvatore Claudio Fanni is a young radiologist and a PhD candidate in Clinical and Translational Sciences at the University of Pisa. He has published articles indexed in PubMed, contributed chapters for international publishers, and edited a book titled “Introduction to Artificial Intelligence.” He has also actively participated in national and international conferences. His primary research interests include thoracic radiology, particularly quantitative CT, and applications of Natural Language Processing in radiology. Salvatore is a member of the Young Club Committee of the European Society of Medical Imaging and Informatics, and he collaborates with the Imaging Lab in Pisa on various EU projects.

Jingyu Zhong, MD, Shanghai Jiao Tong University School of Medicine, China

Jingyu Zhong is a radiologist at Tongren Hospital, Shanghai Jiao Tong University School of Medicine. His main research interests include musculoskeletal and abdominal radiology, radiomics and artificial intelligence in medical imaging, and radiological research methodology. He is a member of the Scientific Editorial Board of the journal BMC Medical Imaging and other journals.

About the Collection

BMC Artificial Intelligence is calling for submissions to our Collection, AI in radiology: revolutionizing medical imaging and interpretation.

The integration of artificial intelligence (AI) in radiology has transformed the landscape of medical imaging and interpretation. AI-powered algorithms and machine learning techniques are being increasingly used to enhance diagnostic accuracy, automate image interpretation, and optimize radiology workflows. These advancements have the potential to revolutionize clinical practice, improve patient outcomes, and streamline healthcare delivery.

It is crucial for us to continue advancing our collective understanding in this area to harness the full potential of AI in radiology. Recent advances have demonstrated the efficacy of AI in detecting abnormalities in medical images, extracting imaging biomarkers, and facilitating rapid and precise diagnosis. Furthermore, AI has shown promise in improving the efficiency of radiology workflows, reducing interpretation times, and enhancing the overall quality of patient care.

We invite contributions that examine a wide range of topics relating to the application of AI in radiology, including but not limited to:

AI-powered diagnosis in radiology
Imaging data analysis using machine learning
Radiology workflow optimization with AI
Automated image interpretation and diagnostic support
AI applications in medical imaging technology
AI-driven innovations in radiology equipment
AI-based solutions for personalized medicine
Ethical considerations in AI integration in radiology

Please email Alison Cuff, the editor for BMC Artificial Intelligence, (alison.cuff@biomedcentral.com) if you would like more information before you submit.

This Collection supports and amplifies research related to SDG 3: Good Health & Well-Being, SDG 9: Industry, Innovation & Infrastructure.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "AI in radiology: revolutionizing medical imaging and interpretation" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.