The Digital Doctor
How Digital Health Can Transform Healthcare
- 1st Edition - January 15, 2025
- Editor: Chayakrit Krittanawong
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 7 2 8 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 4 3 4 4 - 5
The Digital Doctor: How Digital Health Can Transform Healthcare discusses digital health and demonstrates the appropriateness of each technology using an evidence-based approa… Read more
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Request a sales quoteThe Digital Doctor: How Digital Health Can Transform Healthcare discusses digital health and demonstrates the appropriateness of each technology using an evidence-based approach. It serves as a comprehensive summary on current, evidence-based digital health applications, future novel digital health technologies (e.g., mobile health, blockchain, web3.0), as well as some of the current challenges and future directions for digital health within the various medical subspecialties. This book is a comprehensive review of digital health for clinicians, researchers, bioinformatic students, biomedical engineers interested in this topic.
- Provides a history and overview of the various modalities of digital health and their application within each field of medicine as narrated by leading experts
- Discusses current digital health-based medical research, including landmark trials within each field of medicine
- Addresses current knowledge gaps that clinicians commonly face that often prevent the application of digital health-based research to clinical practice
- Provides examples of specific cases and discusses challenges and biases associated with digital health
Clinicians (all specialties), academicians, healthcare professionals, researchers, students, engineers, scientists, and practitioners working in medical research using artificial intelligence and machine learning for predicting trends of various diseases
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Section I. Introduction of digital health in healthcare
- Chapter 1. Transforming primary care through digital health technology
- Introduction
- Major categories of digital health
- Current examples of digital health intervention/implementation in primary care
- Behavioral modifications and chronic disease prevention
- Screening
- Informing evidence-based healthcare: Risk assessment
- Current challenges preventing digital health intervention in primary care
- Future directions with an emphasis on how digital health intervention can help solve specific problems in primary care
- Chapter 2. AI-powered blockchain technology in healthcare
- Introduction
- Foundations of AI-powered blockchain in healthcare
- Decentralization and its impact on healthcare
- Overview of AI algorithms and their compatibility with blockchain
- Synergies between AI and blockchain in healthcare
- Use cases of AI-powered blockchain in healthcare
- Secure health data management
- Drug traceability and supply chain management
- AI-enhanced diagnostics and treatment
- Implementing AI-powered blockchain in healthcare
- Technical consideration
- Infrastructure requirements
- Smart contracts and consensus mechanisms
- Regulatory challenges and solutions
- Navigating legal frameworks
- Ensuring compliance and ethical AI use
- Challenges and opportunities
- Potential roadblocks
- Opportunities for growth
- Conclusion
- Chapter 3. Blockchain applications in healthcare
- Introduction
- Blockchain in new spheres
- Across industries
- Health care applications
- Data
- Tracking
- Monitoring
- Solution space
- Technology selection algorithm
- Our technology selection algorithm is described below
- Selection algorithm
- Limitations
- Conclusion
- Chapter 4. Mobile health apps: Current state, barriers, and future directions
- Introduction
- Current state
- Acceleration of mobile health apps and adoption
- Telehealth development and commercialization
- Remote patient monitoring
- Virtual fitness
- Rise of the condition-specific digital therapeutics
- Advantages and use cases
- Randomized control studies in digital therapeutics
- Advances in regulatory approval and commercialization
- Barriers
- Digital inequity
- Lack of comprehensive regulatory supervision
- Lack of clear reimbursement mechanism
- Privacy and security concerns
- Future directions
- Integration of digital health information into the electronic health system
- Empowerment by artificial intelligence
- Greater data transparency
- App prescription
- Conclusion
- Chapter 5. Telemedicine origins, current state, and future prospects
- Brief history
- Modalities of telemedicine
- Impediments for future development
- Take aways
- Chapter 6. Telenutrition: Clinical applications and future directions
- Introduction
- Chronic disease management
- Oncology
- Chronic kidney disease and hemodialysis
- Home care
- Reported challenges of telenutrition
- Future directions
- Conclusion/take aways
- Chapter 7. Digital health regulation in the US from the FDA perspective
- Introduction
- FDA and digital health
- Background
- Navigating digital health policies
- FDA's digital health policies related to section 520(o) of the FD&C act
- General wellness products
- Medical device data systems
- Clinical decision support (CDS) software
- Device software functions (DSFs) and Mobile Medical Applications (MMA)
- Artificial intelligence and machine learning (AI/ML) in medical device software
- Good Machine Learning Practice
- Cybersecurity
- International Medical Device Regulators Forum and software as a medical device
- Concluding thoughts
- Chapter 8. Artificial intelligence in precision space health
- Introduction
- Human spaceflight as an opportunity for AI-driven precision medicine systems
- Considerations for AI application in precision space medicine
- Crew health and performance and clinical decision support systems
- Human system integration
- Technical and data considerations
- Biomedical/clinical considerations
- Epistemological considerations
- Programmatic/legal considerations
- Future directions: Summarized objectives for developing an AI-powered precision space health system
- Conclusion
- Chapter 9. Metaverse and healthcare
- Introduction
- Current examples
- Medical professional-facing examples
- Patient-facing examples
- Medical professional-facing and patient-facing example
- Current challenges
- Technical limitations
- Privacy and security concerns
- Regulatory barriers
- Accessibility issues
- Integration challenges
- Future directions
- Personalized healthcare experiences
- Advanced medical education and training
- Enhanced patient engagement and adherence
- Remote monitoring and early detection
- Bridging healthcare disparities
- Conclusion
- Major takeaways
- Chapter 10. Artificial intelligence and public health
- Introduction
- Self-driving cars and car-related accidents
- Social media misinformation
- Air pollution
- HIV/AIDS
- Obesity
- Diabetes
- Cancer
- Opioid crisis
- Illicit drug use
- Loneliness
- Limitations
- Formal methods for validation of AI based techniques
- Conclusion
- Section II. Digital health in specialties
- Chapter 11. The role of digital health in dermatology
- Introduction
- Current examples
- Telehealth services
- Artificial intelligence
- Challenges
- Future directions
- Major takeaways
- Chapter 12. Digital health in oncology
- Introduction
- Potential applications
- Telemedicine
- Drug development and clinical trial optimization
- Digital innovation (predictive analytics+precision medicine)
- Future considerations
- Data standardization and regulation
- Improving digital health literacy and access
- Conclusion
- Chapter 13. Ophthalmic digital health
- Introduction
- Visual acuity measurement
- Ophthalmic camera and smartphones
- Amblyopia detection devices
- Artificial intelligence for ophthalmic screening
- Current challenges for digital health and AI/ML-enabled technologies in ophthalmology
- Datasets
- Trust
- Clinical reference standard
- Real world performance monitoring
- Ethics, equity, and bias
- Other challenges preventing adoption of AI into ophthalmic medical practice
- The future of digital health in ophthalmology
- Collaborative Communities
- Optical coherence tomography
- Digital Imaging and Communications in Medicine (DICOM)
- Managing health equity, bias, and privacy
- Diabetic retinopathy
- Diabetic macular edema
- Age-Related Macular Degeneration
- Glaucoma
- Retinopathy of Prematurity
- Cataract
- Ocular melanoma
- Expanding the reach of ophthalmic clinical practice
- Concluding thoughts
- Chapter 14. Evidence and impact of digital interventions in nephrology
- Introduction
- Patient-end digital interventions in nephrology
- Current challenges preventing digital health intervention in nephrology
- Future directions
- Major takeaways (three to five, one sentence each)
- Chapter 15. Digital health in neurology: Advancements, applications, and impact
- Introduction
- Digital health in neurology
- Wearable devices and robotics
- Personalized medicine and neurology
- Cerebrovascular disease
- Epilepsy and electroencephalography
- Sleep medicine
- Multiple sclerosis
- Dementia and cognitive disorders
- Limitations of digital health applications
- Future directions
- Key messages
- Chapter 16. Digital health in gastroenterology
- Introduction discussing the role of digital health, including a review of the landmark trials, within gastroenterology
- Current examples of digital health intervention/implementation in gastroenterology (published works to date)
- Inflammatory bowel disease
- Hepatology
- Mobile applications
- Current challenges preventing digital health intervention in gastroenterology
- Future directions with an emphasis on how digital health intervention can help solve specific problems in gastroenterology
- Artificial intelligence and machine learning
- Telementoring
- Equity and access
- Major takeaways (three to five, one sentence each)
- Chapter 17. Digital health in hepatology
- Introduction
- Current examples of digital health implementation in hepatology
- Digital health devices and telemedicine
- Artificial intelligence
- Current challenges preventing digital health intervention in hepatology
- Future directions with an emphasis on how digital health intervention can help solve problems in hepatology
- Major takeaways
- Chapter 18. Digital interventions for mental health care
- Introduction
- Case examples of successful digital mental health interventions
- Telepsychiatry
- Online platforms
- Smartphone apps
- Supporting clinicians and health systems
- Challenges facing the field of digital mental health
- New directions for digital mental health
- Conclusions
- Chapter 19. Digitalization in orthopedics
- Introduction
- Prevention
- Pre-hospital and outpatient setting
- Resuscitation room
- Clinical assessment
- Radiology
- Surgery
- Follow-up and rehabilitation
- Teaching and education
- Outlook
- Chapter 20. Artificial intelligence and digital health in Anesthesiology
- Introduction
- Challenges of applying and implementing AI in anesthesiology
- Data quality
- Transparency and reliability
- Regulations
- Ethical concerns
- Workflow and reliance on technology
- Conflicts of interest
- Biases
- Recommendations
- Conclusion
- Chapter 21. Digital genetics and genomics
- Introduction
- Current examples of digital health intervention in genetics
- Challenges preventing digital health implementation in the field of genetics
- Future directions
- Final remarks and major takeaways
- Chapter 22. Digital health in cardiac surgery
- Introduction
- Current examples of digital health intervention/implementation in cardiac surgery
- Current challenges preventing digital health intervention in cardiac surgery
- Future directions with an emphasis on how digital health intervention can help solve specific problems in cardiac surgery
- Major takeaways
- Chapter 23. Digital health in surgery
- An introduction to digital surgery
- Current examples of digital surgery
- Preoperative risk scoring
- Surgical time prediction
- Preoperative planning
- Computer vision
- Next generation surgical robotics
- Surgical education
- Augmented and virtual reality
- Current challenges preventing digital health intervention in surgery
- Future directions
- Major takeaways
- Section III. Case study
- Chapter 24. Digital health in end-stage kidney disease
- Introduction
- Role of digital health, review of landmark trials
- Current examples of digital health intervention/implementation
- Blood pressure management
- Nutritional counseling
- Kidney transplantation
- Pathology
- Peritoneal dialysis
- General nephrology
- Nephrology care in rural hospitals
- Current challenges preventing digital health intervention
- Future directions
- Summary
- Major takeaways
- Chapter 25. Digital health in GERD
- Introduction
- Discussion
- Chapter 26. Digital health for cardiovascular disease and diabetes
- Introduction
- Current examples of digital health in cardiovascular disease and diabetes
- General cardiovascular disease prevention
- Primary prevention
- Secondary prevention
- Arrhythmia screening and management
- Diabetes management
- Insulin titration and self-care
- Interstitial glucose monitoring
- Insulin delivery
- Lessons from negative studies
- Challenges in implementation of digital health
- The ABCDE framework to assess novel digital health interventions
- Assess the intervention
- Benefit to patients and clinical practice
- Clinical integration
- Data governance, storage, and privacy
- Engagement and retention
- Health equity
- Future directions
- Major takeaways
- Chapter 27. Digital health in psoriasis
- Artificial intelligence
- Teledermatology
- Mobile device applications
- Methods
- Results
- Diagnosis
- Artificial intelligence
- Teledermatology
- Mobile device applications
- Clinical management
- Artificial intelligence
- Teledermatology
- Mobile device applications
- Discussion
- Artificial intelligence
- Teledermatology
- Mobile device applications
- Conclusion
- Limitations
- Chapter 28. Digital health in pituitary surgery
- Introduction
- Preoperative assessments
- Diagnosis
- Current and future challenge
- Surgical decision
- Current and future challenge
- Surgical planning
- Current and future challenge
- Enhancing surgical efficiency
- Navigation
- Current and future challenge
- Visualization
- Current and future challenge
- Instruments
- Current and future challenge
- Decision support
- Current and future challenge
- Postoperative care/follow up
- Inpatient outcome
- Current and future challenge
- Outpatient follow up
- Current and future challenge
- Conclusion
- Section IV. Practical session
- Chapter 29. Practical session in machine learning for healthcare: Step by step guide to beginners
- Introduction
- Python
- Data cleaning and preliminary analysis
- Visualizations and exploratory data analysis
- Preprocessing for machine learning: Coding our variables
- Splitting our data into train and test sets
- Aside on reading documentation
- Examining how the model learns
- Training different machine learning models
- R
- Data cleaning and preliminary analysis
- Visualizations and exploratory data analysis
- Preprocessing for machine learning: Dataset balancing
- Preprocessing data in R
- Predicting individuals who might develop coronary artery disease
- Random forest model
- KNN model
- Notes and tips for future model training
- Index
- No. of pages: 496
- Language: English
- Edition: 1
- Published: January 15, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443157288
- eBook ISBN: 9780443343445
CK
Chayakrit Krittanawong
Dr. Chayakrit Krittanawong is a leading authority in AI and space health research in medicine. He’s one of the few cardiologists chosen from all over the world to oversee, commission, and review the American College of Cardiology Expert Consensus Decision Pathway and the American College of Cardiology/American Heart Association (ACC/AHA) guidelines on Performance Measures and ACC/ AHA Task Force on Clinical Data Standards. He is also a writing committee of the American College of Cardiology and the American Heart Association Clinical Practice Guidelines. He is a clinician-scientist interested in precision space medicine, using integrative omics and emerging technologies such as artificial intelligence and blockchain to assist physicians and patients worldwide. His primary research interests include bioinformatics, big data analytics, multiomics, machine learning, deep learning, digital health, wearable technology, and preventive medicine. Dr. Krittanawong is an author of more than 230 peer-reviewed publications and a coinventor of many medical patents. He’s received numerous awards from various societies and organizations for his work in artificial intelligence, medical physics, and medical imaging. He’s lectured at the American College of Cardiology and the American Heart Association in the United States and abroad.
Affiliations and expertise
Cardiologist, NYU Langone Medical Center, New York, USA