Medical Image Computing in the Era of Artificial Intelligence and Digital Innovation

Medical imaging has undergone a profound transformation through the integration of artificial intelligence, particularly machine learning and deep learning methods, which are increasingly used to enhance image acquisition, reconstruction, and interpretation. Recent advances in multimodal data fusion and radiomics have enabled more comprehensive extraction of clinically relevant information from heterogeneous imaging and patient data. Despite these developments, challenges remain in ensuring robustness, interpretability, and clinical reliability of AI systems in real-world diagnostic and therapeutic settings.The goal of this Research Topic is to advance the development and clinical translation of artificial intelligence methods for medical imaging, with a focus on improving diagnostic accuracy, workflow efficiency, and personalized treatment strategies. In particular, we aim to promote innovative approaches in image analysis, multimodal data fusion, AI-based reconstruction, radiomics, and explainable AI for clinical decision support and image-guided interventions.Suitable themes for manuscripts include, but are not limited to:• Novel machine learning and deep learning approaches for medical image analysis, including segmentation, classification, detection, and registration tasks.• AI-based image reconstruction methods aimed at improving image quality, reducing noise, and minimizing acquisition time or radiation exposure.• Multimodal data fusion techniques combining medical imaging with clinical, genomic, or physiological data for improved diagnostic and prognostic modeling.• Radiomics and deep radiomics approaches for extracting quantitative imaging biomarkers for disease characterization and treatment response prediction.• Explainable AI (XAI) methods that improve transparency, interpretability, and clinical trust in diagnostic and decision-support systems.• Robust and generalizable AI models for diagnostic applications across different imaging modalities, populations, and clinical environments.• AI-assisted image-guided surgery, including real-time navigation, intraoperative decision support, and preoperative planning systems.• Development and validation of large-scale, high-quality medical imaging datasets, including multimodal and longitudinal data resources.• Clinical translation studies evaluating the performance, safety, and effectiveness of AI-based imaging solutions in real-world healthcare settings.• Ethical, regulatory, and data governance aspects of deploying AI in medical imaging and personalized healthcare systems.This Research Topic welcomes original research articles, methodological studies, clinical validation studies, reviews, data reports, and perspective articles contributing to the advancement of AI-driven medical imaging. Topic editors: Thomas Martin Deserno, Heinz Handels Source: https://www.frontiersin.org/research-topics/83527/medical-image-computing-in-the-era-of-artificial-intelligence-and-digital-innovation