Chapter 9 Nanorobots in healthcare
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Iram Fatima
Abstract
By 2030, approximately 5 billion individuals might be on the brink of losing their access to essential healthcare services, encompassing compulsory medical care, vital medications, and interactions with healthcare professionals. This challenge could escalate if the shortage of adequately trained healthcare workers worsens, especially at a time when their demand is at an all-time high. Recognizing this gap is crucial, underscoring the urgent necessity to actualize advanced medical technology for the future. Among the spectra of cutting-edge medical practices, equipment, and strategies, one of the most revolutionary emerging technologies in healthcare today is nanobots. Nanobots, also referred to as nanorobots, are minute robots with sizes ranging from 1 to 100 nm, which is one-tenth the size of a typical blood cell. Within the healthcare domain, nanobots are primarily oriented toward medical diagnostics and surveillance. These miniature robots are introduced into the body through direct injection into the bloodstream, where they function as an internal monitoring system for the human body. They possess the ability to detect changes in the surrounding environment and assess molecular structures. Moreover, nanobots are capable of identifying potential health issues. When combined with advanced software platforms, nanobots have the potential to serve as a powerful diagnostic and monitoring tool. Medical professionals use nanobots to continuously track a patient’s health status, gather information about their dietary requirements, and administer medications in real time.
Abstract
By 2030, approximately 5 billion individuals might be on the brink of losing their access to essential healthcare services, encompassing compulsory medical care, vital medications, and interactions with healthcare professionals. This challenge could escalate if the shortage of adequately trained healthcare workers worsens, especially at a time when their demand is at an all-time high. Recognizing this gap is crucial, underscoring the urgent necessity to actualize advanced medical technology for the future. Among the spectra of cutting-edge medical practices, equipment, and strategies, one of the most revolutionary emerging technologies in healthcare today is nanobots. Nanobots, also referred to as nanorobots, are minute robots with sizes ranging from 1 to 100 nm, which is one-tenth the size of a typical blood cell. Within the healthcare domain, nanobots are primarily oriented toward medical diagnostics and surveillance. These miniature robots are introduced into the body through direct injection into the bloodstream, where they function as an internal monitoring system for the human body. They possess the ability to detect changes in the surrounding environment and assess molecular structures. Moreover, nanobots are capable of identifying potential health issues. When combined with advanced software platforms, nanobots have the potential to serve as a powerful diagnostic and monitoring tool. Medical professionals use nanobots to continuously track a patient’s health status, gather information about their dietary requirements, and administer medications in real time.
Kapitel in diesem Buch
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XV
- Chapter 1 The impact of blockchain technology on the healthcare system 1
- Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
- Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
- Chapter 4 Role of artificial intelligence in disease diagnosis 89
- Chapter 5 Machine learning for twinning the human body 105
- Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
- Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
- Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
- Chapter 9 Nanorobots in healthcare 209
- Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
- Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
- Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
- Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
- Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
- Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
- Index 375
Kapitel in diesem Buch
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XV
- Chapter 1 The impact of blockchain technology on the healthcare system 1
- Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
- Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
- Chapter 4 Role of artificial intelligence in disease diagnosis 89
- Chapter 5 Machine learning for twinning the human body 105
- Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
- Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
- Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
- Chapter 9 Nanorobots in healthcare 209
- Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
- Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
- Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
- Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
- Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
- Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
- Index 375
