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Future of AI in medical imaging

Chanderwal, Nitin, (1978-)

Future of AI in medical imaging

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Future of AI in medical imagingAvinash Kumar Sharma, Nitin Chanderwal, editors.
其他題名:	Future of artificial intelligence in medical imaging
其他作者:	Chanderwal, Nitin,
出版者:	Hershey, Pennsylvania :IGI Global,2024.
面頁冊數:	1 online resource (312 p.)
標題:	Diagnostic imagingMethods.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-2359-5
ISBN:	9798369323601$q(ebook)

Future of AI in medical imaging
Future of AI in medical imaging[electronic resource] /Future of artificial intelligence in medical imagingAvinash Kumar Sharma, Nitin Chanderwal, editors. - Hershey, Pennsylvania :IGI Global,2024. - 1 online resource (312 p.)

Includes bibliographical references and index.

Chapter 1. Use of AI in medical image processing -- Chapter 2. Internet of things for smart healthcare: a survey -- Chapter 3. Insightful visions: how medical imaging empowers patient-centric healthcare -- Chapter 4. A medical comparative study evaluating electrocardiogram signal-based blood pressure estimation -- Chapter 5. Comparative analysis of machine learning-based diabetes prediction approaches -- Chapter 6. Counterfeit medicine detection using blockchain technology -- Chapter 7. Blockchain-based intelligent, interactive healthcare systems -- Chapter 8. Impact of machine learning and deep learning techniques in autism -- Chapter 9. Web-based application for physical to digital ECG signal analysisfor cardiac dysfunctions -- Chapter 10. Real-time symptomatic disease predictor using multi-layer perceptron -- Chapter 11. Mental health monitoring in the digital age: a comprehensive analysis -- Chapter 12. Emerging, assistive, and digital technology in telemedicine systems -- Chapter 13. Lung cancer classification using deep learning hybrid model -- Chapter 14. Advancing healthcare: economic implications of immediate MRI in suspected scaphoid fractures - a comprehensive exploration -- Chapter 15. Digital twin-based smart healthcare services for the next generation society.

"Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes.Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes."--

ISBN: 9798369323601$q(ebook)Subjects--Topical Terms:

974236
Diagnostic imaging
--Methods.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: RC78.7.D53 / F88 2024e

Dewey Class. No.: 616.07/540285

National Library of Medicine Call No.: WN 180 / .F88 2024e

Future of AI in medical imaging
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504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Use of AI in medical image processing -- Chapter 2. Internet of things for smart healthcare: a survey -- Chapter 3. Insightful visions: how medical imaging empowers patient-centric healthcare -- Chapter 4. A medical comparative study evaluating electrocardiogram signal-based blood pressure estimation -- Chapter 5. Comparative analysis of machine learning-based diabetes prediction approaches -- Chapter 6. Counterfeit medicine detection using blockchain technology -- Chapter 7. Blockchain-based intelligent, interactive healthcare systems -- Chapter 8. Impact of machine learning and deep learning techniques in autism -- Chapter 9. Web-based application for physical to digital ECG signal analysisfor cardiac dysfunctions -- Chapter 10. Real-time symptomatic disease predictor using multi-layer perceptron -- Chapter 11. Mental health monitoring in the digital age: a comprehensive analysis -- Chapter 12. Emerging, assistive, and digital technology in telemedicine systems -- Chapter 13. Lung cancer classification using deep learning hybrid model -- Chapter 14. Advancing healthcare: economic implications of immediate MRI in suspected scaphoid fractures - a comprehensive exploration -- Chapter 15. Digital twin-based smart healthcare services for the next generation society.
520 3 $a "Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes.Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes."-- $c Provided by publisher.
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650 0 $a Diagnostic imaging $x Digital techniques. $3 191915
650 0 $a Artificial intelligence. $3 194058
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700 1 $a Chanderwal, Nitin, $d 1978- $3 974232
700 1 $a Sharma, Avinash Kumar, $d 1982- $3 974233
700 1 $a Tyagi, Shobhit, $d 1996- $3 974234
700 1 $a Upadhyay, Prashant. $3 974235
710 2 $a IGI Global. $3 529832
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