Revolutionizing healthcare
-
G. Jenifa
Abstract
Artificial intelligence (AI)artificial intelligence (AI) and, in particular, machine learning (ML) have recently assumed the role of leading disruptive technologies in the area of biomaterials science and engineering by providing new avenues for material design and property prediction and enabling the development of personalized medicinepersonalized medicine. This chapter reviews the interdisciplinary field of ML with biomaterials engineering, focusing on using algorithms to fine-tune biomaterial properties and outcomes. One of the key areas of interest is the interpretation of hyperparameter optimization for performance enhancement, another is the increasing relevance of ML for improving the efficiency of characterization methods, and the third one is the creation of simulation models to predict property optimization. Some trends that are expected to unfold in the future include the use of multimodal data, new approaches to better process real-time data, and an improved ability to provide clearer explanations for models. The potential ethical issues and regulatory concerns of utilizing ML in biomaterials are also highlighted, along with the emphasis on sustainability and our responsibility to use these technologies responsibly in the biomaterials sector. Thus, this chapter demonstrates how the biomaterialsbiomaterials research and applications domains are being revolutionized through the use of ML and presents a wealth of information to researchers and practitioners.
Abstract
Artificial intelligence (AI)artificial intelligence (AI) and, in particular, machine learning (ML) have recently assumed the role of leading disruptive technologies in the area of biomaterials science and engineering by providing new avenues for material design and property prediction and enabling the development of personalized medicinepersonalized medicine. This chapter reviews the interdisciplinary field of ML with biomaterials engineering, focusing on using algorithms to fine-tune biomaterial properties and outcomes. One of the key areas of interest is the interpretation of hyperparameter optimization for performance enhancement, another is the increasing relevance of ML for improving the efficiency of characterization methods, and the third one is the creation of simulation models to predict property optimization. Some trends that are expected to unfold in the future include the use of multimodal data, new approaches to better process real-time data, and an improved ability to provide clearer explanations for models. The potential ethical issues and regulatory concerns of utilizing ML in biomaterials are also highlighted, along with the emphasis on sustainability and our responsibility to use these technologies responsibly in the biomaterials sector. Thus, this chapter demonstrates how the biomaterialsbiomaterials research and applications domains are being revolutionized through the use of ML and presents a wealth of information to researchers and practitioners.
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
