AI in Medical Imaging and Image Processing: Volume I

This compilation emphasizes the transformative role of artificial intelligence (AI) and machine learning (ML) in the healthcare field, illustrating their potential to refine diagnostics, treatment protocols, and patient management. The studies confront critical healthcare challenges, presenting solutions that improve precision, efficiency, and accessibility. Featured are works on enhancing diagnostics, employing models like convolutional neural networks (CNNs) and transformers for the early and accurate identification of various conditions, such as different cancers, stroke, intracranial hemorrhage, and acute aortic syndrome. The collection also delves into AI applications in surgical planning and intraoperative guidance, with research analyzing preoperative imaging predictors and AI tools for detecting surgical wound infections. New AI methodologies for addressing rare and complex diagnoses, such as early-stage osteosarcoma detection, bone mineral density screening in cystic fibrosis, and biomarker identification in leukemia, are included. Additionally, the compilation addresses the practical aspects of AI integration, such as interrater variability, reproducibility, and the necessity for standardized benchmarks. This collection serves as a valuable resource for healthcare professionals, researchers, and technologists aiming to comprehend and utilize AI's potential in medicine.