AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies.
The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. iii 4. Contents - p. v 5. Contributors - p. ix 6. Foreword - p. xi 7. AI and Data Science in Medical Research: An Overview - p. xiii 8. Related Titles - p. xv 9. Part I Foundations and core technologies - p. 1 10. Chapter 1 Medical data foundations: Key concepts and definitions for clinicians and researchers - p. 3 11. Chapter 2 Artificial intelligence in medical research: Fundamental methods, techniques, and clinical applications - p. 31 12. Part II AI-driven diagnosis and patient monitoring - p. 81 13. Chapter 3 Artificial intelligence in neuroimaging: From data acquisition to data analysis - p. 83 14. Chapter 4 Synthetic data for melanoma detection: Generative adversarial networks and diffusion models in practice - p. 113 15. Chapter 5 Voice-based deep learning for Parkinson's disease diagnosis - p. 131 16. Chapter 6 Modeling Mpox transmission dynamics: Deterministic approaches linking environment and host interactions - p. 151 17. Part III Therapeutics, genomics, and ethical perspectives - p. 175 18. Chapter 7 Artificial intelligence for drug discovery and development - p. 177 19. Chapter 8 Revolutionizing genomic medicine with artificial intelligence and data analytics - p. 199 20. Chapter 9 Artificial intelligence-powered pipelines for therapeutic innovation - p. 243 21. Chapter 10 Conclusions and future directions: Challenges, opportunities, and ethical considerations in artificial intelligence-driven medical research - p. 263 22. Index - p. 265
AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies.
The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. iii 4. Contents - p. v 5. Contributors - p. ix 6. Foreword - p. xi 7. AI and Data Science in Medical Research: An Overview - p. xiii 8. Related Titles - p. xv 9. Part I Foundations and core technologies - p. 1 10. Chapter 1 Medical data foundations: Key concepts and definitions for clinicians and researchers - p. 3 11. Chapter 2 Artificial intelligence in medical research: Fundamental methods, techniques, and clinical applications - p. 31 12. Part II AI-driven diagnosis and patient monitoring - p. 81 13. Chapter 3 Artificial intelligence in neuroimaging: From data acquisition to data analysis - p. 83 14. Chapter 4 Synthetic data for melanoma detection: Generative adversarial networks and diffusion models in practice - p. 113 15. Chapter 5 Voice-based deep learning for Parkinson's disease diagnosis - p. 131 16. Chapter 6 Modeling Mpox transmission dynamics: Deterministic approaches linking environment and host interactions - p. 151 17. Part III Therapeutics, genomics, and ethical perspectives - p. 175 18. Chapter 7 Artificial intelligence for drug discovery and development - p. 177 19. Chapter 8 Revolutionizing genomic medicine with artificial intelligence and data analytics - p. 199 20. Chapter 9 Artificial intelligence-powered pipelines for therapeutic innovation - p. 243 21. Chapter 10 Conclusions and future directions: Challenges, opportunities, and ethical considerations in artificial intelligence-driven medical research - p. 263 22. Index - p. 265
