Artificial Intelligence and Medical Image Processing

Artificial Intelligence (AI) has rapidly emerged as a transformative technology in healthcare, particularly in medical image processing. Medical imaging modalities such as MRI, CT, PET, ultrasound, and digital pathology produce vast amounts of complex data essential for diagnosis, treatment planning, and disease monitoring. Traditional manual analysis of these images can be time-consuming, subjective, and prone to variability. AI techniques, especially deep learning and neural networks, offer powerful tools to automate and enhance image analysis, enabling more accurate, efficient, and reproducible results. These methods support critical tasks including image segmentation, classification, detection, and enhancement, ultimately assisting clinicians in making timely and informed decisions. The integration of AI into medical imaging promises to improve diagnostic accuracy, streamline clinical workflows, and facilitate personalized medicine, marking a significant advance in healthcare delivery.

Despite significant advances in medical imaging technologies, the increasing volume and complexity of imaging data pose challenges for accurate and timely interpretation. Manual analysis remains labor-intensive and susceptible to variability among clinicians, which can impact diagnostic consistency and patient outcomes. The goal of this Research Topic is to address these challenges by advancing the development and application of Artificial Intelligence (AI) techniques that enhance medical image processing. Specifically, it aims to promote research that improves the accuracy, efficiency, and reliability of image analysis through innovative AI-driven methods such as deep learning and neural networks.Achieving this goal requires interdisciplinary efforts that combine computer science, biomedical engineering, and clinical expertise. Research should focus on creating robust AI models capable of handling diverse imaging modalities and clinical scenarios, as well as ensuring their interpretability, generalizability, and ethical use in real-world settings. Additionally, the goal includes fostering the translation of AI research into clinical practice by validating algorithms with large-scale, multi-institutional datasets and addressing regulatory and privacy concerns. Ultimately, this Research Topic seeks to accelerate the integration of AI-powered medical image processing tools that support better diagnostics and improve patient care.

This Research Topic covers advancements in Artificial Intelligence applied to medical image processing across diverse imaging modalities and clinical domains. Specific themes of interest include deep learning for image segmentation and classification, AI-driven computer-aided diagnosis (CAD), multimodal image fusion, interpretable and explainable AI models, and the clinical deployment of AI tools. Contributions exploring ethical considerations, data privacy, and bias mitigation in AI applications are also encouraged.We welcome original research articles, comprehensive reviews, methods papers, and case studies that provide novel insights or practical implementations. Manuscripts should clearly demonstrate technical rigor, clinical relevance, and include validation on representative datasets. Interdisciplinary works bridging computer science, biomedical engineering, and clinical practice are highly encouraged to foster translational impact. All submissions will undergo peer review following the standards of Frontiers in Artificial Intelligence.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI), Medical Image Analysis, Deep Learning, Computer-Aided Diagnosis (CAD), Medical Imaging, Machine Learning, Image Recognition, Diagnostic Imaging, Pattern Recognition, Healthcare Technology

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.