Evolving challenges and breakthroughs in deep learning-driven medical imaging

Deep learning and artificial intelligence are driving a transformative era in medical imaging, ushering in advanced tools for diagnosis, prognosis, and individualized patient care. Building on the foundation and momentum established in Volume 1 of the Research Topic Deep Learning for Medical Imaging Applications, this second volume highlights continued progress in leveraging AI to process complex imaging data, detect subtle and clinically significant patterns, and support data-driven clinical decisions. Although recent studies demonstrate improved diagnostic accuracy and provide new insights into disease mechanisms, clinical adoption remains limited. This is largely due to concerns about transparency, reliability, generalizability, and the ethical use of AI in real-world clinical settings. The community continues to debate, test, and refine deep learning systems, yet persistent challenges such as data quality, privacy constraints, interpretability, and the absence of standardized evaluation metrics require further investigation.

This Research Topic aims to provide an inclusive and dynamic platform for imaging researchers, clinicians, and technology specialists to present innovative research, critical analyses, and multidisciplinary perspectives on the application of deep learning in medical imaging. Our objectives are to advance the theoretical and practical understanding of AI-driven imaging methods, rigorously evaluate algorithmic performance and scalability, and promote best practices for implementation in varied healthcare contexts. We especially encourage contributions that address transparency and interpretability, enhance accessibility for non-technical clinical stakeholders, and systematically analyze bias and methods to mitigate it. Through this collaborative effort, we seek to accelerate the creation and adoption of robust, adaptable, and trustworthy AI tools tailored to the unique demands of different healthcare environments.

To gather insights into both established and emerging applications of deep learning in medical imaging, we welcome articles - including Original Research, Review, Perspective, Case Report, Method, Brief Research Report, Opinion, Policy and Practice Review, and Technology and Code - that address, but are not limited to, the following themes:
• Recent advances in deep learning across imaging modalities such as X-ray, CT, MRI, ultrasound, PET, fluoroscopy, and natural images
• Development and training of AI models for large-scale and complex medical image datasets
• Detection, analysis, and mitigation of algorithmic bias and strategies for learning from limited or imbalanced data
• Multi-modality and multi-instrument AI models for heterogeneous clinical datasets and real-world deployment
• Enhancing explainability, transparency, and usability of AI models for healthcare professionals from diverse backgrounds
• Generative and synthesis models in medical imaging, including data augmentation and simulation
• Investigating the ethical, privacy, and regulatory implications of AI-based technologies in clinical contexts

Article types and fees

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

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• ... 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
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Explainable AI, Medical Data Generation, AI Diagnostic Tools, Machine Learning, Healthcare AI, Data Annotation, Computational Medicine, PET, CT, FUS, Endoscopy, Radiomics, Neural Networks (CNNS), Computer-Aided Diagnosis (CAD), Re

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.