Machine learning innovations in biomedical materials from drug discovery to personalized medicine
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P. Nagarajan
Abstract
Machine learning (ML)machine learning has also introduced new paradigms to biomedical materials science for new material discovery, material optimization, and customized treatment. Pertinent to this, this chapter presents a comprehensive review of the progress and applications of ML in fabricating biomedical materials, including drug discovery and patient-specific treatment. Some of the unambiguously critical areas of ML, which are transfer learning, predictive modeling, and high-throughput screening, are discussed from the point of view of how they can be useful in improving material performance and efficiency. Despite this, the focus is on presenting use cases, especially the application of ML toward improving the systems used for drug delivery, the scaffolds employed for tissue engineering, and the “tailoring” of medical treatmentsmedical treatments. Besides offering solutions to such crucial questions as data integration, model interpretability, and computational requirements, the chapter provides readers with an idea of the future directions for research and advancement. This chapter shows how ML is a revolutionary method in biological material science and in improving patients’ lives by bridging the gap between computational and clinicalclinical utilization.
Abstract
Machine learning (ML)machine learning has also introduced new paradigms to biomedical materials science for new material discovery, material optimization, and customized treatment. Pertinent to this, this chapter presents a comprehensive review of the progress and applications of ML in fabricating biomedical materials, including drug discovery and patient-specific treatment. Some of the unambiguously critical areas of ML, which are transfer learning, predictive modeling, and high-throughput screening, are discussed from the point of view of how they can be useful in improving material performance and efficiency. Despite this, the focus is on presenting use cases, especially the application of ML toward improving the systems used for drug delivery, the scaffolds employed for tissue engineering, and the “tailoring” of medical treatmentsmedical treatments. Besides offering solutions to such crucial questions as data integration, model interpretability, and computational requirements, the chapter provides readers with an idea of the future directions for research and advancement. This chapter shows how ML is a revolutionary method in biological material science and in improving patients’ lives by bridging the gap between computational and clinicalclinical utilization.
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
