AI-powered sensors and devices for sustained health tracking
-
P. Prasant
and
A.S. Vigneshwar
Abstract
The steady progress made in artificial bio interfaces and sensingsensing systems has strong impacts on the development of continuous health monitoring systems in biomedical sensor technology solutions. In this chapter, the authors focus on enhancing biomedical sensors and devices using AI and underlining the need to incorporate complex algorithms and real-time analysis of large datasets. Some of the focal areas described include the utilization of smart sensor nodes and machine learning for diagnostic prognosis, and the optimization of data credibility. As the chapter seeks to discuss the future of health monitoring technologies in coming up with an improved advanced remote health monitoring system, it discusses the challengeschallenges in sensor calibration, data processing, and real-time feedback. The future prospects and challenges of personalized medicine and preventative care resulting from the direct application of AI to patient care are also described in relation to the potential for dramatically changing patient management and treatment algorithms.
Abstract
The steady progress made in artificial bio interfaces and sensingsensing systems has strong impacts on the development of continuous health monitoring systems in biomedical sensor technology solutions. In this chapter, the authors focus on enhancing biomedical sensors and devices using AI and underlining the need to incorporate complex algorithms and real-time analysis of large datasets. Some of the focal areas described include the utilization of smart sensor nodes and machine learning for diagnostic prognosis, and the optimization of data credibility. As the chapter seeks to discuss the future of health monitoring technologies in coming up with an improved advanced remote health monitoring system, it discusses the challengeschallenges in sensor calibration, data processing, and real-time feedback. The future prospects and challenges of personalized medicine and preventative care resulting from the direct application of AI to patient care are also described in relation to the potential for dramatically changing patient management and treatment algorithms.
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
