Artificial Intelligence in Clinical Practice

Artificial Intelligence in Clinical Practice: How AI Technologies Impact Medical Research and Clinics compiles current research on Artificial Intelligence within medical subspecialties, helping practitioners with diagnosis, clinical decision-making, disease prediction, prevention, and the facilitation of precision medicine. The book defines the basic concepts of big data and AI in medicine and highlights current applications, challenges, ethical issues, and biases. Each chapter discusses AI applied to a specific medical subspecialty, including primary care, preventive medicine, general internal medicine, radiology, pathology, infectious disease, gastroenterology, cardiology, hematology, oncology, dermatology, ophthalmology, mental health, neurology, pulmonary, critical care, rheumatology, surgery, and OB-GYN.

This is a valuable resource for clinicians, students, researchers and members of medical and biomedical fields who are interested in learning more about artificial intelligence technologies and their applications in medicine.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Foreword
Chapter 1. Artificial intelligence in primary care
Abstract
Introduction
Potentials
Limitations and challenges
Conclusion
References
Chapter 2. Artificial intelligence in general internal medicine
Abstract
Introduction of artificial intelligence in general medicine
High-value care
Perioperative management
Patient safety and quality issues
Current challenges preventing artificial intelligence application
Future directions
Major takeaway points
References
Chapter 3. Artificial intelligence devices and assessment in medical imaging
Abstract
Introduction
Examples of artificial intelligence implementation in medical imaging
Regulatory framework
Evaluation framework
Challenges in artificial intelligence/machine learning device evaluation studies
Transparency
Conclusions
References
Chapter 4. Artificial intelligence in anatomical pathology
Abstract
Introduction
Overview of digital anatomical pathology workflow
Applications of artificial intelligence in anatomic pathology
Conclusions
References
Chapter 5. Artificial intelligence in clinical microbiology
Abstract
Major takeaways
References
Chapter 6. Artificial intelligence on interventional cardiology
Abstract
Introduction
Intravascular imaging (intravascular ultrasound/optical coherence tomography)
Future directions
Major takeaway points
References
Chapter 7. Artificial intelligence in heart failure and transplant
Abstract
Overview of artificial intelligence and machine learning
Artificial intelligence in heart failure
Artificial intelligence in cardiac transplantation
Current limitations of artificial intelligence in heart failure and transplant care
Future work
Major takeaways
Disclosure
References
Chapter 8. Artificial intelligence in hematology
Abstract
References
Chapter 9. Artificial intelligence in oncology
Abstract
Introduction
Artificial intelligence research topics in oncology
Methodology in artificial intelligence oncology research
Cancer data: opportunities and challenges
Conclusion
References
Chapter 10. Artificial intelligence in ophthalmology I: retinal diseases
Abstract
Artificial intelligence in diabetic retinopathy
Artificial intelligence in age-related macular degeneration
Artificial intelligence in retinopathy of prematurity
Artificial intelligence in retinal vein occlusion
Challenges and future
Conclusion
References
Chapter 11. Artificial intelligence in ophthalmology II: glaucoma
Abstract
Role of artificial intelligence in glaucoma
Current challenges and limitations
Future considerations
Conclusion
References
Chapter 12. Artificial intelligence in ophthalmology III: systemic disease prediction
Abstract
Introduction
Artificial intelligence-based ocular image analysis for cardiovascular disease
Artificial intelligence-based ocular image analysis for renal diseases
Artificial intelligence-based ocular image analysis for neurological diseases
Artificial intelligence-based ocular image analysis for other systemic diseases
Current challenges
Future directions
Major takeaway points
References
Chapter 13. Artificial intelligence in respiratory medicine
Abstract
Introduction discussing the role of artificial intelligence, including a review of the landmark trials, within your field
Current examples of artificial intelligence implementation in clinical practice (published works to date using artificial intelligence in your area of specialty)
Current challenges preventing artificial intelligence application
Future directions with an emphasis on how artificial intelligence can help solve specific problems within your field
Major takeaways (3–5, one sentence each)
References
Chapter 14. Artificial intelligence in critical care
Abstract
Introduction
Artificial intelligence applications in critical care
Conclusion
References
Chapter 15. Artificial intelligence in dermatopathology
Abstract
Introduction
Summary of literature
Challenges and opportunities
Future directions
References
Chapter 16. Artificial intelligence in infectious diseases
Abstract
Introduction
Artificial intelligence for clinical microbiology
Artificial intelligence for the clinical diagnosis and management of infected patients
Challenges for artificial intelligence in infectious diseases
Conclusion
References
Chapter 17. Artificial intelligence in neglected tropical diseases
Abstract
Present problems in neglected tropical diseases
Dilemma in diagnosis of neglected tropical disease
Applications of artificial intelligence in neglected tropical disease diagnosis
Challenges of artificial intelligence in neglected tropical disease diagnosis
Future implications of artificial intelligence in neglected tropical disease diagnosis
References
Chapter 18. Artificial intelligence in psychiatry: current practice and major challenges
Abstract
Introduction
Examples of artificial intelligence implementation in clinical practice
Current limitations and challenges preventing artificial intelligence application
Future directions
Conclusion
References
Chapter 19. Application of artificial intelligence frameworks in the clinical practice of neurology: recent advances and future directions
Abstract
Introduction
Current state of artificial intelligence in clinical neurology
Deployment of artificial intelligence frameworks in healthcare: the ABC cycle
Conclusions
Major takeaways
References
Chapter 20. Artificial intelligence in rheumatology
Abstract
Introduction
Current examples of artificial intelligence implementation in rheumatology
Current challenges
Robust models require sufficient high-quality data
External validation with independent datasets
Future direction
Key messages
References
Chapter 21. Artificial intelligence in endocrinology
Abstract
Introduction
Challenges to adoption
The future of artificial intelligence in endocrinologist daily practice
Major takeaway points
Acknowledgments
Funding
References
Chapter 22. Artificial intelligence in sleep medicine
Abstract
Introduction
Current examples of artificial intelligence implementation in clinical practice
Current challenges
Future directions
Major takeaway points
References
Chapter 23. Artificial intelligence in nephrology
Abstract
Introduction
Examples of artificial intelligence implementation in nephrology
Challenges and future directions
Major takeaway points
References
Chapter 24. Artificial intelligence in surgery
Abstract
Introduction
Preoperative planning
Intraoperative use
Postoperative care
Conclusion
Major takeaway points
References
Chapter 25. Artificial intelligence in cardiothoracic surgery: current applications and future perspectives
Abstract
Introduction
Applications of artificial intelligence in cardiothoracic surgery
Current challenges and future direction of artificial intelligence applications in cardiothoracic surgery
Conclusion
Major takeaway points
Acknowledgments
References
Chapter 26. Artificial intelligence in orthopedics
Abstract
Introduction
Current challenges
Major takeaway points
References
Chapter 27. Artificial intelligence in plastic surgery
Abstract
Introduction
Artificial intelligence implementation in clinical practice
Current challenges
Future directions
Major takeaway points
References
Chapter 28. Artificial intelligence in obstetrics and gynecology
Abstract
Current applications of artificial intelligence in obstetrics
Electronic health record analysis for applications of artificial intelligence
Current applications of artificial intelligence in gynecology
Future challenges and opportunities for artificial intelligence in obstetrics and gynecology
References
Chapter 29. Artificial intelligence in urology
Abstract
Introduction (role of artificial intelligence, landmark trials in urology)
Current examples of artificial intelligence implementation in urology
Challenges preventing artificial intelligence application
Future directions
Major takeaway points
Abbreviations
References
Chapter 30. Artificial intelligence in neurosurgery—a focus on neuro-oncology
Abstract
Introduction
Current examples
Current challenges preventing artificial intelligence application
Future directions
Major takeaway points
References
Chapter 31. Artificial intelligence in vascular surgery
Abstract
Introduction
Artificial intelligence applications in vascular surgery
Artificial intelligence in vascular diagnostics
Perioperative medicine, risk stratification, and outcome prediction
Conclusions
References
Chapter 32. Artificial intelligence in neonatal and pediatric intensive care units
Abstract
Artificial intelligence in pediatrics
Current challenges preventing artificial intelligence application
Recommendations
Future steps
Major takeaway points
References
Chapter 33. Artificial intelligence in pediatrics
Abstract
Introduction
Current implementation examples
Current challenges
Future directions
Major takeaway points
References
Chapter 34. Artificial intelligence in pediatric congenital and acquired heart disease
Abstract
Introduction
Clinical assessment
Imaging
Future applications
Conclusion
References
Chapter 35. Artificial intelligence in anesthesiology
Abstract
Introduction
Predictive analytics
Image analysis
Smart devices
Pain management
Education
Quality and administration
Future directions
References
Chapter 36. Artificial intelligence in emergency medicine
Abstract
References
Chapter 37. Artificial intelligence in allergy and immunology
Abstract
Introduction
Current examples of artificial intelligence implementation in allergy and immunology
Challenges with artificial intelligence in allergy and immunology
Future directions
Conclusion
Major takeaways
References
Chapter 38. Artificial intelligence in medical genetics
Abstract
References
Chapter 39. Artificial intelligence in healthcare: a perspective from Google
Abstract
References
Chapter 40. Artificial intelligence drives the digital transformation of pharma
Abstract
From protein to prescription
Artificial intelligence in digital health—digital pathology is leading the way
Artificial intelligence for drug discovery and design
Artificial intelligence for clinical trial design
Artificial intelligence for drug manufacturing and distribution
Summary and trends
References
Chapter 41. Artificial intelligence in regulatory decision-making for drug and biological products
Abstract
Disclaimer
References
Chapter 42. Machine learning applications in toxicology
Abstract
Potential for artificial intelligence in clinical toxicology
Detection and treatment of substance use disorder
Clinical diagnostic support for toxicology
Toxicovigilance
Current challenges and future directions
Major take away points
References
Chapter 43. Artificial intelligence in adverse drug events
Abstract
Introduction
Current rule-based systems
Next generation of artificial intelligence tools
Current challenges limiting implementation
Future directions
Major takeaway points
References
Chapter 44. Artificial intelligence in mass spectrometry-based proteomics
Abstract
Introduction
Artificial intelligence in the proteomics workflow
Artificial intelligence to distill insight from proteomics data
Current challenges of artificial intelligence in proteomics
Toward artificial intelligence in proteomics
References
Chapter 45. Artificial intelligence and global health
Abstract
Introduction
Disease outbreaks
Hazard identification
Clinical decision support systems
Challenges related to the use of artificial intelligence in global health
Future directions
Disclaimer
References
Chapter 46. Legal aspects of artificial intelligence in medical practice
Abstract
Introduction
Upstream
Midstream
Downstream
Conclusion
Chapter 47. Socioeconomic bias in applying artificial intelligence models to health care
Abstract
Artificial intelligence bias
Socioeconomic status as a key social determinant of health
Measuring socioeconomic status
Socioeconomic status and artificial intelligence bias
Mitigation
Conclusion
References
Chapter 48. Artificial intelligence in dermatology
Abstract
Introduction
Clinical disease classification
Dermatopathology
Point-of-care diagnosis and telehealth
Precision medicine
Current limitations and future directions
Major takeaway points
References
Chapter 49. Artificial intelligence in gastroenterology and hepatology
Abstract
Introduction
Esophagus
Stomach
Small intestine
Colon
Liver
Pancreas
Limitations and future directions
References
Chapter 50. Artificial intelligence in nutrition research
Abstract
Dietary assessment
Precision nutrition
Public health in nutrition
Current challenges and limitations
Conclusion
Major takeaway points
References
Chapter 51. Artificial intelligence in cardiac electrophysiology
Abstract
Introduction
Artificial intelligence to guide management of atrial fibrillation
Artificial intelligence to guide management of ventricular arrhythmias and sudden cardiac death
Challenges and future directions for the use of artificial intelligence in electrophysiology
Summary
References
Index

How AI Technologies Impact Medical Research and Clinics

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Chayakrit Krittanawong