Fundamentals of AI for Medical Education, Research and Practice provides a comprehensive introduction on all aspects of AI application in healthcare, ranging from medical education to diagnostic medicine. The book's chapters are grouped into six sections: an introduction to AI in healthcare, AI in medical education, AI healthcare in research, AI in clinical practice, and future directions and challenges, concluding with practical application of AI tools in health care. Written by an experienced physiologist and medical educationist who is actively involved in all aspects of teaching and learning, this book will prove to be immense benefit for medical researchers, practicing clinicians, academicians and medical students at all levels.
1. Part 1. Introduction to AI in healthcare - p. 0 2. Introduction - p. 1 3. Chapter 1. Timeline of artificial intelligence - p. 3 4. Chapter 2. Understanding artificial intelligence (AI) in healthcare and medical education - p. 27 5. Chapter 3. Overview of AI technologies and their impact on healthcare - p. 45 6. Chapter 4. Ethical and regulatory considerations in AI adoption in healthcare - p. 73 7. Part 2. AI in medical education - p. 94 8. Introduction - p. 95 9. Chapter 5. Integration of AI into medical curriculum: Challenges and opportunities - p. 97 10. Chapter 6. AI-enabled simulation and virtual learning environments - p. 117 11. Chapter 7. AI-based personalized learning in medical education - p. 145 12. Chapter 8. AI tools in assessment - p. 167 13. Part 3. AI in healthcare research - p. 186 14. Introduction - p. 187 15. Chapter 9. AI in clinical research: Transforming methodologies and discoveries - p. 189 16. Chapter 10. Data science and AI techniques in healthcare research - p. 209 17. Chapter 11. AI-driven drug discovery and development - p. 233 18. Chapter 12. Predictive analytics and AI in epidemiological studies - p. 257 19. Part 4. AI in clinical practice - p. 276 20. Introduction - p. 277 21. Chapter 13. AI in diagnostics: Enhancing accuracy and efficiency - p. 279 22. Chapter 14. AI-enabled decision support systems in clinical practice - p. 305 23. Chapter 15. Robotics and AI-assisted surgery: Advancements and applications - p. 331 24. Chapter 16. AI-driven personalized health care - p. 355 25. Part 5. Future directions and challenges - p. 378 26. Introduction - p. 379 27. Chapter 17. AI in telemedicine and remote patient monitoring - p. 381 28. Chapter 18. Emerging trends in AI in healthcare: Innovations and opportunities - p. 409 29. Chapter 19. Challenges and limitations of AI adoption in healthcare - p. 429 30. Chapter 20. Training the future healthcare workforce for AI integration - p. 451 31. Part 6. Practical application of AI tools in healthcare - p. 466 32. Introduction - p. 467 33. Chapter 21. Practical training of AI tools in medical education - p. 469 34. Chapter 22. Practical training of AI tools in clinical research - p. 485 35. Chapter 23. Practical training of AI tools in healthcare work force - p. 503 36. Index - p. 525
Fundamentals of AI for Medical Education, Research and Practice provides a comprehensive introduction on all aspects of AI application in healthcare, ranging from medical education to diagnostic medicine. The book's chapters are grouped into six sections: an introduction to AI in healthcare, AI in medical education, AI healthcare in research, AI in clinical practice, and future directions and challenges, concluding with practical application of AI tools in health care. Written by an experienced physiologist and medical educationist who is actively involved in all aspects of teaching and learning, this book will prove to be immense benefit for medical researchers, practicing clinicians, academicians and medical students at all levels.
1. Part 1. Introduction to AI in healthcare - p. 0 2. Introduction - p. 1 3. Chapter 1. Timeline of artificial intelligence - p. 3 4. Chapter 2. Understanding artificial intelligence (AI) in healthcare and medical education - p. 27 5. Chapter 3. Overview of AI technologies and their impact on healthcare - p. 45 6. Chapter 4. Ethical and regulatory considerations in AI adoption in healthcare - p. 73 7. Part 2. AI in medical education - p. 94 8. Introduction - p. 95 9. Chapter 5. Integration of AI into medical curriculum: Challenges and opportunities - p. 97 10. Chapter 6. AI-enabled simulation and virtual learning environments - p. 117 11. Chapter 7. AI-based personalized learning in medical education - p. 145 12. Chapter 8. AI tools in assessment - p. 167 13. Part 3. AI in healthcare research - p. 186 14. Introduction - p. 187 15. Chapter 9. AI in clinical research: Transforming methodologies and discoveries - p. 189 16. Chapter 10. Data science and AI techniques in healthcare research - p. 209 17. Chapter 11. AI-driven drug discovery and development - p. 233 18. Chapter 12. Predictive analytics and AI in epidemiological studies - p. 257 19. Part 4. AI in clinical practice - p. 276 20. Introduction - p. 277 21. Chapter 13. AI in diagnostics: Enhancing accuracy and efficiency - p. 279 22. Chapter 14. AI-enabled decision support systems in clinical practice - p. 305 23. Chapter 15. Robotics and AI-assisted surgery: Advancements and applications - p. 331 24. Chapter 16. AI-driven personalized health care - p. 355 25. Part 5. Future directions and challenges - p. 378 26. Introduction - p. 379 27. Chapter 17. AI in telemedicine and remote patient monitoring - p. 381 28. Chapter 18. Emerging trends in AI in healthcare: Innovations and opportunities - p. 409 29. Chapter 19. Challenges and limitations of AI adoption in healthcare - p. 429 30. Chapter 20. Training the future healthcare workforce for AI integration - p. 451 31. Part 6. Practical application of AI tools in healthcare - p. 466 32. Introduction - p. 467 33. Chapter 21. Practical training of AI tools in medical education - p. 469 34. Chapter 22. Practical training of AI tools in clinical research - p. 485 35. Chapter 23. Practical training of AI tools in healthcare work force - p. 503 36. Index - p. 525
