Artificial Intelligence and Advanced Digital Processing Techniques to Improve Biomedical Imaging

Over the past few decades, artificial intelligence has steadily become a major driver of innovation in the field of medical imaging, transforming how clinical images are acquired, analyzed, and interpreted. Advanced machine learning, deep learning, and computational techniques are enabling more accurate diagnoses, faster clinical decision-making, and increasingly personalized treatments. At the same time, the integration of digital systems and intelligent devices has opened new avenues for non-invasive, scalable, and cost-effective diagnostic solutions. Despite the remarkable progress achieved, the use of these technologies in personalized healthcare continues to evolve rapidly, presenting substantial opportunities for further research and development.

The goal of this Research Topic is to promote the interdisciplinary integration of biomedical imaging, artificial intelligence, and digital innovation. By encouraging contributions focused on the development of new methodologies and advanced applications, it aims to provide a platform for the development of reliable and sustainable diagnostic solutions, based on advanced computational models and tailored to individual patient needs. The overall goal is to improve personalized health management and optimize clinical outcomes through the informed and responsible use of artificial intelligence in biomedical imaging.

Suitable themes for manuscripts include (but are not limited to):

• Advanced automated bioimage analysis approaches.
• Development of new biomedical image databases and multimodal datasets.
• Multimodal processing of images, biological signals, and clinical data.
• Reliable and transparent machine learning techniques for diagnostic applications.
• AI-based reconstruction techniques to reduce or eliminate the need for contrast agents and ionizing radiation.
• AI-enhanced optical, fluorescence, and microscopy imaging approaches for extracting diagnostic and quantitative features from tissue and cellular structures.
• AI-enhanced radiomics and deep radiomics, including automated pipelines for extracting and integrating quantitative biomarkers.
• Explainable AI (XAI) specifically for medical imaging, to improve model interpretability by radiologists and clinicians.
• AI for treatment planning and image-guided surgery, including models for intraoperative navigation or treatment optimization.
• Emerging AI applications for imaging, such as photoacoustics, digital holography, quantum imaging, and hyperspectral imaging.

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
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... 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
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: AI, Medical imaging, Optical imaging, Biomedical optics, Biophotonics, Microscopy and Fluorescence imaging, Auto image analysis, Multimodal data fusion, Machine learning for diagnostics, AI-based image reconstruction, Radiomics, XAI, Image-guided surgery

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.