About this Research Topic
Given the success of Research Topic Artificial Intelligence-Assisted Medical Imaging Solutions for Integrating Pathology and Radiology Automated Systems and the rapidly evolving subject area, we are pleased to announce the launch of Volume II.
Recent advances in Artificial Intelligence (AI) have attracted interest of pathology and radiology experts. Cancer diagnostic method developments often make use of information gleaned from both the pathology and radiography fields. A radiology and pathology diagnostic reporting system that incorporates text, sentinel images, and molecular diagnostic data into a consistent interpretation would be beneficial for making decisions regarding the further development of those diagnostic methods. AI can now be widely found being used in various diagnostics, including in radiology, cancer, and infectious disease. It's important to recognize that clinical diagnosis, translational research, and basic research are interconnected and may benefit from AI's enhanced capabilities. AI-assisted systems are being developed by researchers all over the world for pathology and radiology medical modalities such as magnetic resonance imaging (MRI), ultrasound, medical radiation, angiography, and computed tomography (CT) scanners, microscopy imaging, whole slide image, telepathology. These automated systems can help in fast and accurate disease identification, which can increase the chances of a more precise diagnosis for patients.
The goal of this Research Topic is to identify the basic research issues associated with pathology and radiology around the increased use of AI technologies. It is also to gain a deeper insight into the potential applications of AI technologies in the real world. We encourage professionals working in these disciplines to submit articles on but not limited to the following fields:
• AI models for the precision diagnosis for integrated pathology and radiology systems
• Explainable transfer learning on medical data for integrated pathology and radiology systems
• Lightweight deep learning models for pathology and radiology
• Reconstruction, restoration or enhancement of the medical images such as Medical (i.e. CT, MRI, Ultrasound) Image reconstruction, Multi-modality Medical (i.e. CT, MRI, Ultrasound) image fusion, Medical image denoising
• Analysis of Diagnostic Radiology, Interventional radiology, and Radiation oncology
• Deep learning-based medical image classification, segmentation, recognition, and registration
• Brain, Chest, Breast, Cardiac, and Musculoskeletal imaging using deep learning
This Research Topic welcomes all article types available in the Pathology section of Frontiers in Medicine including Brief Research Reports, Case Reports, Clinical Trials, General Commentary, Methods, Mini Reviews, Original Research, Perspective, Review, Study Protocol, and Systematic Review.
Recent advances in Artificial Intelligence (AI) have attracted interest of pathology and radiology experts. Cancer diagnostic method developments often make use of information gleaned from both the pathology and radiography fields. A radiology and pathology diagnostic reporting system that incorporates text, sentinel images, and molecular diagnostic data into a consistent interpretation would be beneficial for making decisions regarding the further development of those diagnostic methods. AI can now be widely found being used in various diagnostics, including in radiology, cancer, and infectious disease. It's important to recognize that clinical diagnosis, translational research, and basic research are interconnected and may benefit from AI's enhanced capabilities. AI-assisted systems are being developed by researchers all over the world for pathology and radiology medical modalities such as magnetic resonance imaging (MRI), ultrasound, medical radiation, angiography, and computed tomography (CT) scanners, microscopy imaging, whole slide image, telepathology. These automated systems can help in fast and accurate disease identification, which can increase the chances of a more precise diagnosis for patients.
The goal of this Research Topic is to identify the basic research issues associated with pathology and radiology around the increased use of AI technologies. It is also to gain a deeper insight into the potential applications of AI technologies in the real world. We encourage professionals working in these disciplines to submit articles on but not limited to the following fields:
• AI models for the precision diagnosis for integrated pathology and radiology systems
• Explainable transfer learning on medical data for integrated pathology and radiology systems
• Lightweight deep learning models for pathology and radiology
• Reconstruction, restoration or enhancement of the medical images such as Medical (i.e. CT, MRI, Ultrasound) Image reconstruction, Multi-modality Medical (i.e. CT, MRI, Ultrasound) image fusion, Medical image denoising
• Analysis of Diagnostic Radiology, Interventional radiology, and Radiation oncology
• Deep learning-based medical image classification, segmentation, recognition, and registration
• Brain, Chest, Breast, Cardiac, and Musculoskeletal imaging using deep learning
This Research Topic welcomes all article types available in the Pathology section of Frontiers in Medicine including Brief Research Reports, Case Reports, Clinical Trials, General Commentary, Methods, Mini Reviews, Original Research, Perspective, Review, Study Protocol, and Systematic Review.
Keywords: Pathology, Radiology, Computer-Aided Diagnosis, Explainable AI, Generalized models, Volume II
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
