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AI Disease Detection: Advancements and Applications
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AI Disease Detection: Advancements and Applications in Chattanooga, TN
Current price: $130.00
Barnes and Noble
AI Disease Detection: Advancements and Applications in Chattanooga, TN
Current price: $130.00
Loading Inventory...
Size: Hardcover
Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection
AI in Disease Detection
discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.
Sample topics explored in
include:
Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
AI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness
Delivering excellent coverage of the subject,
is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.
AI in Disease Detection
discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.
Sample topics explored in
include:
Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
AI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness
Delivering excellent coverage of the subject,
is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.
Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection
AI in Disease Detection
discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.
Sample topics explored in
include:
Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
AI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness
Delivering excellent coverage of the subject,
is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.
AI in Disease Detection
discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.
Sample topics explored in
include:
Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
AI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness
Delivering excellent coverage of the subject,
is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.
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