Efficient AI for Radiology Imaging Analysis

Artificial intelligence (AI) is reshaping radiology by enhancing diagnostic accuracy, automating image interpretation, and supporting clinical decision-making. However, many AI models are computationally intensive, limiting their usability in routine clinical practice, especially in resource-constrained or time-sensitive environments. As radiology advances toward more personalized approaches, integrating AI with radiomics and precision medicine holds tremendous promise. Yet, this integration also introduces significant demands on data processing and infrastructure. There is a growing need for AI systems that combine high diagnostic performance with computational efficiency, enabling seamless integration into clinical workflows while supporting individualized patient care.

The goal of this Research Topic is to highlight the development and clinical translation of efficient AI methods for radiological imaging analysis. We aim to showcase approaches that reduce computational load, inference time, and resource usage without compromising clinical utility. This includes the design and validation of lightweight models, optimization of radiomics pipelines, and integration of AI into real-time or routine diagnostic workflows. Particular attention is given to methods that contribute to precision medicine, such as imaging-based risk stratification, outcome prediction, or therapy response assessment. Submissions should emphasize clinical relevance, modality robustness across imaging types such as MRI, CT, X-ray, and ultrasound, and potential for adoption in diverse healthcare settings. This Research Topic aims to advance the field by promoting AI solutions that are not only technically innovative but also scalable, sustainable, and aligned with the goals of personalized radiology.

This Research Topic welcomes contributions from clinicians, researchers, and developers working at the intersection of AI, radiology, and clinical practice. Relevant themes include the development of efficient and interpretable AI models for diagnostic imaging, clinically viable radiomics applications, real-time image analysis systems, and the deployment of AI in acute care or low-resource environments. We are especially interested in work that supports clinical decision-making and contributes to precision health initiatives. Submissions may include original research articles, methods papers, clinical validation studies, reviews, and perspectives. Authors are encouraged to clearly describe the efficiency gains, clinical implications, and translational potential of their work.