The Artificial Intelligence in Radiology section of Frontiers in Radiology aims to provide an open, engaging and fast publishing platform for scholars and practitioners to disseminate their cutting-edge, high-quality research studies in the fast-growing, intersecting fields of artificial intelligence (AI) and radiology.
In the past decade, we have experienced and witnessed the surge of powerful and impactful AI technologies, such as AI sensors, AI models and algorithms, AI chips and AI computing platforms, and their far-reaching applications in every corner of our society. Unsurprisingly, AI has touched upon and started to empower all aspects of radiology including, but not limited to:
AI-empowered biomedical imaging data acquisition,
AI-enabled imaging data processing and visualization,
AI-assisted imaging data interpretation and understanding,
AI-based radiological diagnosis and follow-up,
AI-enhanced interventional radiology
This section invites novel scientific contributions in all aspects of these traditional radiology AI technology development, application, and validation. In addition to AI in diagnostic and interventional radiology, AI aspects of a variety of other related imaging modalities and domains such as optical imaging, nuclear medicine, molecular imaging, ultrasound, digital imaging, radiation therapy, and pathology are considered within the scope of this section. Contributions from multi-modalities and/or full-stack integration of imaging data are particularly welcome.
In a broader sense, AI components related to data science, informatics, and human factors in radiology are within the scope of this section. Examples include, but not limited to:
Natural language processing in radiology report analysis and generation,
Radiology literature recommender systems,
Human-computer interactions for radiologists,
Radiology data sharing,
Any other AI technology and system that can empower, enable, assist and help radiologists in their daily works.
This section differentiates itself from other radiology, imaging, medical image analysis, and informatics journals by looking at AI in radiology in a holistic way, that is, adding AI into every possible aspect of radiology and radiologist’s practice to improve quality, productivity and efficiency.
Indexed in: Google Scholar, CrossRef, CLOCKSS, OpenAIRE
Artificial Intelligence in Radiology welcomes submissions of the following article types: Brief Research Report, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Policy Brief, Review, Specialty Grand Challenge and Systematic Review.
All manuscripts must be submitted directly to the section Artificial Intelligence in Radiology, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
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