Chapter 4 Role of artificial intelligence in disease diagnosis
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Deepak Kumar
Abstract
The medical-related use of artificial intelligence (AI) is one of the significant advancements taking place in the field of healthcare. This chapter highlights major AI technologies and their uses in variety of medical sector for diagnosis of disease that include machine learning (ML), natural language processing, convolutional neural network, and quantum computing. This chapter aims to explore and elaborate upon the complex role that AI plays in the diagnosis of diseases in a range of healthcare specializations by describing the technologies used, applications of AI, benefits, drawbacks, and potential future developments in the field of healthcare. Key advantages of AI include its remarkable speed, precision, and consistency leading to early disease detection and better patient outcomes. The chapter emphasizes the rise in FDA-approved AI medical devices, demonstrating the growing acceptance of AI’s promise in healthcare. Use of AI in various conditions like Alzheimer’s disease diagnosis, cancer diagnosis, cardiac disease, dermatological diagnosis, and genetics. Despite having remarkable advantage with AI in health sector there are certain limitations and challenges associated with it like data privacy, biases, and the continual need for research and development. The future trends in AI for disease diagnosis are explored. AI is transforming the diagnosis of diseases by providing quick and accurate detection in a range of medical fields. AI has potential to dramatically change medical care by improving patient outcomes and diagnostic accuracy. Considering the possibility of personalized medication and proactive preventive measures, AI has potential to drastically change healthcare sector.
Abstract
The medical-related use of artificial intelligence (AI) is one of the significant advancements taking place in the field of healthcare. This chapter highlights major AI technologies and their uses in variety of medical sector for diagnosis of disease that include machine learning (ML), natural language processing, convolutional neural network, and quantum computing. This chapter aims to explore and elaborate upon the complex role that AI plays in the diagnosis of diseases in a range of healthcare specializations by describing the technologies used, applications of AI, benefits, drawbacks, and potential future developments in the field of healthcare. Key advantages of AI include its remarkable speed, precision, and consistency leading to early disease detection and better patient outcomes. The chapter emphasizes the rise in FDA-approved AI medical devices, demonstrating the growing acceptance of AI’s promise in healthcare. Use of AI in various conditions like Alzheimer’s disease diagnosis, cancer diagnosis, cardiac disease, dermatological diagnosis, and genetics. Despite having remarkable advantage with AI in health sector there are certain limitations and challenges associated with it like data privacy, biases, and the continual need for research and development. The future trends in AI for disease diagnosis are explored. AI is transforming the diagnosis of diseases by providing quick and accurate detection in a range of medical fields. AI has potential to dramatically change medical care by improving patient outcomes and diagnostic accuracy. Considering the possibility of personalized medication and proactive preventive measures, AI has potential to drastically change healthcare sector.
Chapters in this book
- 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
Chapters in this book
- 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
