AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice examines the transformative role of AI and data science in improving diagnosis, treatment, and healthcare delivery. It shows how machine learning, deep learning, and advanced signal and image analysis enable breakthroughs in genomics, multi-omics integration, biomedical imaging, EEG-based seizure prediction, and real-time physiological monitoring. The book highlights AI-driven stratification of complex syndromes such as sepsis, stroke, and acute respiratory distress syndrome, demonstrating how data-driven models support early detection, personalized interventions, and actionable clinical decisions.
The volume also presents system-level innovations, including AI-based forecasting for dialysis, blood supply management, and telemedicine optimization. It addresses ethical and regulatory challenges, fairness, transparency, data governance, and clinical validation, providing a practical roadmap for healthcare professionals, engineers, researchers, and policymakers. By integrating responsible, human-centered AI into precision medicine, the book illustrates clear pathways to enhance patient care, improve outcomes, and promote equitable healthcare.
1. Introduction - p. xxiii 2. Part I Foundations of AI, data science, and medical training in precision medicine - p. 1 3. Chapter 1 Artificial intelligence-driven personalized medicine - p. 3 4. Chapter 2 AI revolution in healthcare: Enhancing patient care and outcomes through innovative applications and future prospects - p. 27 5. Chapter 3 Precision medicine, omics, and treatable traits as a paradigm shift toward a promising medical curriculum - p. 49 6. Part II Core perspectives on AI in precision medicine: Genomics, imaging, and drug discovery - p. 65 7. Chapter 4 Distributed deep learning approaches for genomics analysis: A comprehensive review - p. 67 8. Chapter 5 Drug discovery and development: Leveraging AI and data science to accelerate the discovery of novel therapeutic compounds and optimize drug development pipelines - p. 89 9. Chapter 6 Advancements in medical imaging: Harnessing artificial intelligence for early disease detection and diagnosis - p. 115 10. Part III Data-driven AI techniques for diagnosis, prediction, and personalized treatment - p. 133 11. Chapter 7 Artificial intelligence-based seizure prediction using electroencephalography signal-to-image conversion techniques - p. 135 12. Chapter 8 CNN-based retinal disease classification using OCT imaging and EfficientNetB0 - p. 153 13. Chapter 9 Machine learning-based prognostic models for hypertensive acute response for stroke patients in intensive care units and emergency departments - p. 185 14. Chapter 10 Precision medicine in acute respiratory distress syndrome (ARDS) - p. 217 15. Chapter 11 Sepsis and precision medicine: Tailoring interventions using patient-specific data and biomarkers - p. 235 16. Part IV AI-driven optimization of healthcare systems and medical logistics - p. 252 17. Chapter 12 Enhancing Telemedicine and Remote Patient Monitoring with AI and Data Science - p. 255 18. Chapter 13 Enhancing peritoneal dialysis care: Leveraging predictive analytics and artificial intelligence - p. 285 19. Chapter 14 AI-driven blood supply chain management: A reinforcement learning approach - p. 313 20. Part V Emerging trends, ethical challenges, and future perspectives in AI-driven healthcare - p. 346 21. Chapter 15 Chaos theory in AI-driven healthcare systems: Unraveling complex data patterns - p. 349 22. Chapter 16 Ethical challenges of artificial intelligence in critical care - p. 375 23. Chapter 17 Data-driven decision-making in healthcare: Unlocking value through web analytics - p. 395 24. Chapter 18 Implementing responsible artificial intelligence-driven precision medicine in critical care - p. 411 25. Chapter 19 Enrichment strategies in Precision Medicine for future clinical trials - p. 439 26. Chapter 20 Conclusions and future perspectives in precision medicine and predictive analytics - p. 461 27. Index - p. 463
AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice examines the transformative role of AI and data science in improving diagnosis, treatment, and healthcare delivery. It shows how machine learning, deep learning, and advanced signal and image analysis enable breakthroughs in genomics, multi-omics integration, biomedical imaging, EEG-based seizure prediction, and real-time physiological monitoring. The book highlights AI-driven stratification of complex syndromes such as sepsis, stroke, and acute respiratory distress syndrome, demonstrating how data-driven models support early detection, personalized interventions, and actionable clinical decisions.
The volume also presents system-level innovations, including AI-based forecasting for dialysis, blood supply management, and telemedicine optimization. It addresses ethical and regulatory challenges, fairness, transparency, data governance, and clinical validation, providing a practical roadmap for healthcare professionals, engineers, researchers, and policymakers. By integrating responsible, human-centered AI into precision medicine, the book illustrates clear pathways to enhance patient care, improve outcomes, and promote equitable healthcare.
1. Introduction - p. xxiii 2. Part I Foundations of AI, data science, and medical training in precision medicine - p. 1 3. Chapter 1 Artificial intelligence-driven personalized medicine - p. 3 4. Chapter 2 AI revolution in healthcare: Enhancing patient care and outcomes through innovative applications and future prospects - p. 27 5. Chapter 3 Precision medicine, omics, and treatable traits as a paradigm shift toward a promising medical curriculum - p. 49 6. Part II Core perspectives on AI in precision medicine: Genomics, imaging, and drug discovery - p. 65 7. Chapter 4 Distributed deep learning approaches for genomics analysis: A comprehensive review - p. 67 8. Chapter 5 Drug discovery and development: Leveraging AI and data science to accelerate the discovery of novel therapeutic compounds and optimize drug development pipelines - p. 89 9. Chapter 6 Advancements in medical imaging: Harnessing artificial intelligence for early disease detection and diagnosis - p. 115 10. Part III Data-driven AI techniques for diagnosis, prediction, and personalized treatment - p. 133 11. Chapter 7 Artificial intelligence-based seizure prediction using electroencephalography signal-to-image conversion techniques - p. 135 12. Chapter 8 CNN-based retinal disease classification using OCT imaging and EfficientNetB0 - p. 153 13. Chapter 9 Machine learning-based prognostic models for hypertensive acute response for stroke patients in intensive care units and emergency departments - p. 185 14. Chapter 10 Precision medicine in acute respiratory distress syndrome (ARDS) - p. 217 15. Chapter 11 Sepsis and precision medicine: Tailoring interventions using patient-specific data and biomarkers - p. 235 16. Part IV AI-driven optimization of healthcare systems and medical logistics - p. 252 17. Chapter 12 Enhancing Telemedicine and Remote Patient Monitoring with AI and Data Science - p. 255 18. Chapter 13 Enhancing peritoneal dialysis care: Leveraging predictive analytics and artificial intelligence - p. 285 19. Chapter 14 AI-driven blood supply chain management: A reinforcement learning approach - p. 313 20. Part V Emerging trends, ethical challenges, and future perspectives in AI-driven healthcare - p. 346 21. Chapter 15 Chaos theory in AI-driven healthcare systems: Unraveling complex data patterns - p. 349 22. Chapter 16 Ethical challenges of artificial intelligence in critical care - p. 375 23. Chapter 17 Data-driven decision-making in healthcare: Unlocking value through web analytics - p. 395 24. Chapter 18 Implementing responsible artificial intelligence-driven precision medicine in critical care - p. 411 25. Chapter 19 Enrichment strategies in Precision Medicine for future clinical trials - p. 439 26. Chapter 20 Conclusions and future perspectives in precision medicine and predictive analytics - p. 461 27. Index - p. 463
