Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms
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P. Nagarajan
Abstract
Personalized medicine,personalized medicine as a distinct approach to modern healthcare system, is characterized by the targeted adaptation of medical therapy to a patient’s genome, proteome, and metabolome. This chapter outlines the development and the current status of personalized medicine, integrated with focused drug delivery systems and individualized material recipe. The author presents genomic, proteomic, and metabolomic technologies as underlying pillars of diagnostic and therapeutic advancement. Advanced sequencing technologies, high-throughput proteomics, and artificial intelligence technology analyses for enhanced clinical efficacy and resource realization are discussed. In this context, assessments of financial benefits and quality-of-life gains that have been associated with other aspects of targeted methods show their usefulness in reducing the risk of untoward medication interactions, enhancing treatmenttreatment outcomes and shortening the time needed to deliver care. Recommendations are made for future developments, with special focus on how these enhance the further growth of personalized medicine. These involve precision oncology and molecular analysis, advancement in gene editing, and incorporation of digital health assets. Thus, this chapter provides an extensive overview of their future outlook and outlines the ways that shape the advanced form of individualized medicine, impacting the current picture of healthcarehealthcare.
Abstract
Personalized medicine,personalized medicine as a distinct approach to modern healthcare system, is characterized by the targeted adaptation of medical therapy to a patient’s genome, proteome, and metabolome. This chapter outlines the development and the current status of personalized medicine, integrated with focused drug delivery systems and individualized material recipe. The author presents genomic, proteomic, and metabolomic technologies as underlying pillars of diagnostic and therapeutic advancement. Advanced sequencing technologies, high-throughput proteomics, and artificial intelligence technology analyses for enhanced clinical efficacy and resource realization are discussed. In this context, assessments of financial benefits and quality-of-life gains that have been associated with other aspects of targeted methods show their usefulness in reducing the risk of untoward medication interactions, enhancing treatmenttreatment outcomes and shortening the time needed to deliver care. Recommendations are made for future developments, with special focus on how these enhance the further growth of personalized medicine. These involve precision oncology and molecular analysis, advancement in gene editing, and incorporation of digital health assets. Thus, this chapter provides an extensive overview of their future outlook and outlines the ways that shape the advanced form of individualized medicine, impacting the current picture of healthcarehealthcare.
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
