Artificial Intelligence in Medical Imaging
A section of Tomography (ISSN 2379-139X).
We are pleased to introduce a new section on Artificial Intelligence (AI) in Medical Imaging in the MDPI medical journal Tomography. As the field of medical imaging continues to evolve rapidly, the integration of AI technologies holds immense potential to enhance diagnostic accuracy, improve patient outcomes, and advance the overall impact of imaging on scientific and clinical discovery.
Tomography (ISSN 2379-139X) has been at the forefront of publishing original research articles, spanning various aspects of imaging science, from fundamental research to clinical trials. With a specific focus on the advancement of imaging technologies, the journal encompasses cross-sectional imaging modalities such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), as well as optical modalities including bioluminescence, photoacoustic imaging, endomicroscopy, fiber optic imaging, and optical computed tomography. The introduction of the AI in Medical Imaging section reflects the recognition of AI as a transformative force in the field. The aim of this section is to provide a dedicated platform for researchers to disseminate their original contributions on AI methods, algorithms, and applications related to medical imaging. We welcome manuscripts that explore the integration of AI techniques with various imaging modalities, including but not limited to US, CT, MRI, and PET. The scope of the section encompasses a broad range of topics, including the development of AI algorithms for image analysis, computer-aided diagnosis, image segmentation, feature extraction, image reconstruction, and image registration. Furthermore, we invite studies that investigate the interpretability, explainability, and robustness of AI models in medical imaging, as well as research on ethical considerations and regulatory aspects of AI deployment in clinical practice. State-of-the-art reviews are also most welcome to provide comprehensive overviews of the latest advancements and challenges in the field. We encourage submissions that demonstrate the clinical impact and utility of AI in medical imaging, such as improved accuracy in disease detection, quantification of disease progression, treatment response assessment, and personalized medicine.
By establishing this dedicated section, Tomography aims to foster interdisciplinary collaborations and facilitate the integration of AI methodologies with traditional imaging technologies. We believe that the analysis of simultaneous multidimensional and multivariate computational data, coupled with AI techniques, can enable the unique identification of different pathological tissue phenotypes, thereby advancing clinical imaging and paving the way for new discoveries.
We invite researchers and practitioners from academia, industry, and clinical settings to submit their high-quality research to the AI in Medical Imaging section of Tomography. Together, we can accelerate the translation of AI innovations into clinical practice and contribute to the ongoing revolution in medical imaging.
Following special issue within this section is currently open for submissions:
- Bias in Tomography Artificial Intelligence (Deadline: 31 January 2024)