Workflow Optimisation for Radiological Imaging

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Workflow Optimisation for Radiological Imaging is written by Jie-Zhi Cheng and published by Frontiers Media SA. It's available with International Standard Book Number or ISBN identification 2832555888 (ISBN 10) and 9782832555880 (ISBN 13).

Deep learning techniques have been shown to successfully address many medical image technical problems, e.g., segmentation, lesion detection, image registration, etc., better than ever. Accordingly, the development of artificial intelligence (AI) applications in medicine is continuously booming in these years for the attempt to improve various image-related medical procedures and workflows. The applications aim to improve the workup of image diagnosis, optimize the imaging workflow, offload the tedious medical tasks, and provide richer and more holistic cues for treatment planning. Specific prominent applications may range from lesion detections like pulmonary nodules and breast lesions, cancer risk prediction, triaging, abnormality quantification and measurement, disease staging, quality control, imaging improvement, surgical/therapy planning, etc. Thus far, many commercial AI applications have been incorporated into clinical practice in this decade. Meanwhile, many promising clinical validation studies were also published in recent years with a vast amount of evaluation data across multiple institutes and countries. With the further breakthrough of ChatGPT, the trend of AI for medicine seems to be unstoppable and will persist in advancing. However, there are still plenty of unsolved or partially solved medical problems with which AI techniques may help.