Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring
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T. Sripriya
Abstract
Due to the rapid evolution of artificial intelligenceartificial intelligence (AI), current biomedical sensor technology has experienced the evolution of exciting concepts and continuous progress in nonstop health monitoring. The data given in this chapter provide an effective focus on AI driven biological sensors, their growth, incorporation, and impact on society in the modern-day healthcare industry. The AI technologiesAI technologies, including machine learning and edge computing are described as the enablers of sensor performance for real-time data processing and individualized healthcare applications. Finally, the principles of AI-integrated sensors along with examples and case studies from clinical practice and conducted research are presented in the chapter and numerous examples of successful AI applications in chronic diseases, diagnostics accuracy, and remote patient control are provided. It also explores the major issues, such as data privacy issues, integration of different systems, and issues of compliance. Students can derive specific knowledge for discovering the potential of AI in the improvement of biomedical sensors and explore the future developments in the biomedical fieldbiomedical field from this chapter.
Abstract
Due to the rapid evolution of artificial intelligenceartificial intelligence (AI), current biomedical sensor technology has experienced the evolution of exciting concepts and continuous progress in nonstop health monitoring. The data given in this chapter provide an effective focus on AI driven biological sensors, their growth, incorporation, and impact on society in the modern-day healthcare industry. The AI technologiesAI technologies, including machine learning and edge computing are described as the enablers of sensor performance for real-time data processing and individualized healthcare applications. Finally, the principles of AI-integrated sensors along with examples and case studies from clinical practice and conducted research are presented in the chapter and numerous examples of successful AI applications in chronic diseases, diagnostics accuracy, and remote patient control are provided. It also explores the major issues, such as data privacy issues, integration of different systems, and issues of compliance. Students can derive specific knowledge for discovering the potential of AI in the improvement of biomedical sensors and explore the future developments in the biomedical fieldbiomedical field from this chapter.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
