Synopsis
AI rapidly transforms diagnostic radiology, offering powerful tools to enhance image interpretation, streamline workflows, and improve diagnostic accuracy. By utilizing deep learning algorithms trained on medical images, AI systems can detect abnormalities with precision comparable to experienced radiologists in certain contexts. These advancements have found real-world application in areas like chest X-ray analysis, mammography, CT and MRI interpretation, and triage in emergency imaging. Case-based insights demonstrate how AI assists in early disease detection, supports differential diagnosis, and reduces diagnostic errors, contributing to better patient outcomes. However, effective clinical integration requires careful validation, consideration of ethical implications, and collaboration between radiologists and AI developers to ensure technology works with, rather than replaces, human expertise. AI in Diagnostic Radiology: Clinical Applications and Case-Based Insights explores the use of AI in diagnostic radiology to enhance image analysis, improve diagnostic accuracy, and streamline clinical workflows. It explains real-world applications through case-based insights, demonstrating how AI supports radiologists in detecting and interpreting medical conditions. This book covers topics such as medical detection, deep learning, and radiology, and is a useful resource for medical professionals, computer engineers, academicians, researchers, and scientists.
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Hardcover. Condition: new. Hardcover. AI rapidly transforms diagnostic radiology, offering powerful tools to enhance image interpretation, streamline workflows, and improve diagnostic accuracy. By utilizing deep learning algorithms trained on medical images, AI systems can detect abnormalities with precision comparable to experienced radiologists in certain contexts. These advancements have found real-world application in areas like chest X-ray analysis, mammography, CT and MRI interpretation, and triage in emergency imaging. Case-based insights demonstrate how AI assists in early disease detection, supports differential diagnosis, and reduces diagnostic errors, contributing to better patient outcomes. However, effective clinical integration requires careful validation, consideration of ethical implications, and collaboration between radiologists and AI developers to ensure technology works with, rather than replaces, human expertise. AI in Diagnostic Radiology: Clinical Applications and Case-Based Insights explores the use of AI in diagnostic radiology to enhance image analysis, improve diagnostic accuracy, and streamline clinical workflows. It explains real-world applications through case-based insights, demonstrating how AI supports radiologists in detecting and interpreting medical conditions. This book covers topics such as medical detection, deep learning, and radiology, and is a useful resource for medical professionals, computer engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798337358017
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Buch. Condition: Neu. AI in Diagnostic Radiology | Clinical Applications and Case-Based Insights | Praveen Kumar (u. a.) | Buch | Englisch | 2025 | IGI Global | EAN 9798337358017 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 133652093
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - AI rapidly transforms diagnostic radiology, offering powerful tools to enhance image interpretation, streamline workflows, and improve diagnostic accuracy. By utilizing deep learning algorithms trained on medical images, AI systems can detect abnormalities with precision comparable to experienced radiologists in certain contexts. These advancements have found real-world application in areas like chest X-ray analysis, mammography, CT and MRI interpretation, and triage in emergency imaging. Case-based insights demonstrate how AI assists in early disease detection, supports differential diagnosis, and reduces diagnostic errors, contributing to better patient outcomes. However, effective clinical integration requires careful validation, consideration of ethical implications, and collaboration between radiologists and AI developers to ensure technology works with, rather than replaces, human expertise. AI in Diagnostic Radiology: Clinical Applications and Case-Based Insights explores the use of AI in diagnostic radiology to enhance image analysis, improve diagnostic accuracy, and streamline clinical workflows. It explains real-world applications through case-based insights, demonstrating how AI supports radiologists in detecting and interpreting medical conditions. This book covers topics such as medical detection, deep learning, and radiology, and is a useful resource for medical professionals, computer engineers, academicians, researchers, and scientists. Seller Inventory # 9798337358017
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