The integration of artificial intelligence (AI) into medical imaging has revolutionized healthcare, facilitating significant advancements in diagnostics, prognostics, and treatment planning. Among diverse imaging modalities, Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), computed tomography (CT), magnetic resonance imaging (MRI), and digital pathology have particularly benefited from sophisticated AI techniques. Deep learning models, specifically convolutional neural networks (CNNs), have demonstrated exceptional success in automating complex tasks, including tumor detection, organ segmentation, and disease classification in radiology. Similarly, AI-driven analysis of pathology imagery has substantially improved the accuracy and efficiency of cellular and tissue-level diagnostics.
Beyond advancements within individual imaging modalities, the advent of multi-modal analytic strategies offers new opportunities to elucidate complex diseases through the intelligent fusion of data from multiple imaging sources. This approach enhances precise diagnosis and provides a comprehensive understanding of underlying disease mechanisms. Despite these progressive developments, persistent challenges remain concerning interpretability, generalizability, and clinical validation, requiring further research to guarantee the safe and effective deployment of AI in clinical settings.
This Research Topic aims to explore cutting-edge applications and advancements of AI in medical imaging, emphasizing PET, SPECT/ CT, MRI and pathology imaging modalities. It addresses key challenges, supports the development of novel algorithms, and promotes the effective integration of AI and precision medicine into established clinical workflows. By consolidating contributions from researchers, clinicians, and engineers, this collection endeavors to highlight the transformative impact of AI on medical image analysis and underscore its potential for improving personalized patient outcomes.
We invite submissions of original research, review articles, case reports, and clinical trials for this topic. To deepen our understanding and enhance treatments for complex diseases, submissions are encouraged across a broad spectrum of related themes, including but not limited to:
o Deep learning in nuclear medicine techniques and radiology: Innovative AI techniques to increase diagnostic accuracy, image segmentation efficiency, and disease detection capabilities in PET, SPECT/ CT, MRI.
o AI-assisted pathology image analysis: Advancements in cellular and tissue-level diagnostics, feature extraction, and classification techniques.
o Multi-modal medical image analysis: Novel frameworks and algorithms for intelligent fusion, interpretation, and analysis of diverse medical imaging sources.
o Clinical implementation: Studies addressing AI validation, interpretability, generalization across diverse patient cohorts, and integration of precision medicine within clinical settings and workflows.
Keywords: Artificial Intelligence, Deep Learning, Medical Image Analysis, nuclear medicine, precision medicine
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.
