Fundamentals of AI for Medical Education, Research and Practice

1st Edition - January 20, 2025
Imprint: Academic Press
Author: Sameer Mohommed Khan
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 3 5 8 4 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 3 5 8 5 - 3

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… Read more

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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.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Dedication
About the author
Preface
Acknowledgments
Part 1. Introduction to AI in healthcare
Introduction
Chapter 1. Timeline of artificial intelligence
Introduction
Definition of AI
Stages of artificial intelligence
Three stages of artificial intelligence
Basic working of AI
Steps of AI working
Key components of AI
The five key components of AI include
Learning
Reasoning
Problem-solving
Perception
Language understanding
Types of AI
AI systems based on capabilities
Narrow AI
Artificial general intelligence (AGI)
Artificial super AI(ASI)
Based on the learning capabilities AI is also classified as
Machine learning
Deep learning
Reinforcement learning
AI systems based on the functionality
Reactive machines AI
Limited memory AI
Theory of mind
Self-aware AI
AI systems based on application
Natural language processing
Computer vision
Robotics
History of AI
The Greek Connection
Enigma broken with AI (1942)
Birth of AI (1950–56)
Alan Turing and his contribution to artificial intelligence
Turing machine: A model of general-purpose computer
Test for machine intelligence by Alan Turing (1950)
John McCarthy, the father of artificial intelligence
Golden age of AI (1956–74)
The perceptron (1957)
The emergence of deep learning (1962)
The first chatbot—Eliza (1964)
Stanford conference: Shakey
AI winter (1974–93)
Era of GPU (1990–2000)
Man versus machine—Deep Blue beats chess legend (1997)
The birth of machine learning (1997)
Kismet: The first social Robot (1998)
Artificial intelligence robot (1999)
Siri: The virtual assistant (2008)
The Q/A computer system—IBM Watson (2011)
Alexa (2014)
Tianhe-2 (2014)
The first robot citizen—Sophia (2016)
AlphaGo (2016)
Bidirectional encoder representations from transformers (BERT) 2018
Cimon
The revolutionary tool for automated conversations—ChatGPT (2018)
DALL-E (2021)
Microsoft Copilot (2023)
Summary
Chapter 2. Understanding artificial intelligence (AI) in healthcare and medical education
Introduction
Need for AI in healthcare and medical education
Key factors that have led to the adoption of AI in healthcare and medical education include
Technological leap
The real predicament: Big data
Demand for personalized medicine
Diagnostic and therapeutic advancements
AI in geriatric healthcare: The new horizon
Employee burnout
Patient-centric care
Emerging healthcare challenges
Augmenting medical education
Better equipped healthcare professionals
Goals of AI in healthcare
Major goals of AI in healthcare include
Develop problem-solving ability
Promote synergy between humans and AI
Encourage social intelligence
Contemplating the future
In pursuit of knowledge
AI in healthcare
Major areas that AI is being utilized in healthcare are
Predictive analytics
Diagnosis and treatment
Drug discovery and development
Remote patient monitoring
Healthcare management
Robotic surgery
AI in education
Major role of AI in medical education include
Learning intensified
Medical training
Personalized learning
Automated grading and feedback
Increased flexibility and global access
Data analysis and predictive modeling
Language learning and translation
AI reduces cost
AI improves educational equity
AI metaverse
Summary
Chapter 3. Overview of AI technologies and their impact on healthcare
Introduction
AI technologies in healthcare
Artificial intelligence (AI) technologies that are particularly significant for the healthcare include
Machine learning
Types of machine learning
Applications of ML in healthcare
Smart health record
Personalized treatment options
Disease prediction
Drug discovery and development
Disease identification and diagnosis
Medical imaging
Deep learning (DL)
Deep learning and the human brain
Artificial neural network (ANN)
Types of deep learning
Self-care activities
Internet of things (IOT)
Internet of Medical Things (IoMT)
Robotic process automation (RPA)
AI technologies used in medical education
Intelligent tutoring systems (ITS)
Virtual simulations and augmented reality
Experiential learning
Generative AI technologies (GenAI)
Summary
Chapter 4. Ethical and regulatory considerations in AI adoption in healthcare
Introduction
AI ethics
Need for AI ethics
Ethical considerations of medical AI
Data bias
Privacy issue
Accountability and transparency
Reliability and trust
Ethical principles for AI technology in healthcare
Autonomy
Trustworthiness
Data privacy
Accountability and liability
Optimization of data quality
Accessibility, equity, and inclusiveness
Collaboration
Validity
Fairness
AI ethics in medical education
Ethical problems which might arise
Actions must be performed to resolve moral dilemmas
Research ethics pertaining to AI
AI ethics in clinical practice
Actions must be made to resolve moral dilemmas
Regulations in AI usage
Role of the World Health Organization (WHO)
Protect autonomy
Promote human well-being, safety, and public interest
Ensure transparency
Foster accountability
Ensure inclusiveness and equity
Promote AI
UNESCO: Global AI Ethics and Governance Observatory
HIPAA Act
General Data Protection Regulation (GDPR)
AI Act
Creating more ethical AI
Each of the following group of people play an important role in ensuring less bias and risk from AI technologies
Summary
Part 2. AI in medical education
Introduction
Chapter 5. Integration of AI into medical curriculum: Challenges and opportunities
Introduction
Need for AI in medical education
AI in medical teaching and learning
Learner-oriented AI
Instructor-oriented AI
Institution-oriented AI
Tools used in medical teaching and assessment
Chatbots
Applications of chatbots in medical education
Intelligent tutoring system (ITS)
Benefits of ITS in medical education
Virtual patients
Gamification
Some of the benefits of gamification in medical education are
Adaptive learning system
Technology enhanced learning (TEL)
Integration of AI into the curriculum
Medical residents
AI metaverse
AI-based curriculum models
Advantages of integrating AI into the curriculum
Challenges to incorporate AI in medical curriculum
Technical difficulties
Ethical difficulties
A poorly designed curriculum
Education and assessment
Issues with plagiarism
Knowledge disparity among several groups
Evaluation difficulties
Opportunities
Summary
Chapter 6. AI-enabled simulation and virtual learning environments
Introduction
Virtual learning environment
Types of virtual learning
Synchronous
Asynchronous
Hybrid
Applications of VLEs
Provide course materials to students
Encourage cooperative education
Empowering assessment methods
Common virtual learning environments (VLEs) are
Moodle
Blackboard
Simulation-based learning
Reasons for incorporating simulation at all levels of medical teaching
Varieties of simulators
Based on type
Based on fidelity
Based on type
Based on fidelity
Simulation modalities
Computer-based simulation
The procedural stimulation
Simulated clinical immersion
Simulated patients
Hybrid simulation
Virtual reality
History of VR
Types of VR
Nonimmersive VR
Semi-immersive VR
Complete immersion digital reality
WebVR
Augmented reality
Types of AR
Projection based AR
Marker-based AR
Marker less AR
Location-based AR
Superimposition AR
Outlining AR
Difference between AR and VR
Mixed reality
Application of VR and AR in health care
Applications in medical education (Fig. 6.8)
Clinical medicine
Application of AR in health care
Application of mixed reality in medical practice and education
Medical practices
Applications in medical education
Benefits of VLE and simulation-based learning
Easy progress tracking and assessment
Seamless delivery
Customizable learning scenarios
Morale booster
Containing and repeated practice
Provides feedback and evaluation
Enhancing patient education
Simulation training prioritizes patient safety
Customizable learning scenarios
It helps apply academic knowledge
Challenges of simulation-based learning
Lack of learner engagement and perceived realism
Cost
Lack of trained instructors
Building team skills
Student disengagement
Time constraints
Summary
Chapter 7. AI-based personalized learning in medical education
Introduction
Definition
The need for personalized learning in medical education
Goals and aims of personalized learning
Key elements
Student ownership
Flexible learning environments
Individual mastery
Personal learning paths
Learner profiles
Individual goal-setting
Self-assessments
Flexible schedules
Tailored pacing
Classroom modifications
AI-based personalized e-learning systems
Artificial intelligence in education (AIEd)
Role of learning theories and models
Behaviorism
Cognitivism
Constructivism
Connectivism
Humanism
Strategies for implementing personalized e-learning
Adaptive learning
Adaptable learning
Recognize the needs and interests of the learners
Set clear learning objectives
Design varied learning pathways
Provide real-time feedback and support
Foster collaboration and peer learning
Technology and tools for personalization
Learning management systems (LMS)
Adaptive learning platforms
Learning analytics and data
Educational apps and gamification
Artificial generative intelligence (AGI) in personalized education
Adaptive learning
Intelligent tutoring systems
Personalized recommendations
Natural language processing
Data-driven insights
Multimodal learning
Personalized assessment and feedback
Individualized learning paths
Generative AI and e-Learning
Personalized learning experience
Accessibility and inclusion
Learning analytics
Language and translation
Virtual mentors and tutors
Creation of better content
Personalized e-learning platforms
iSpring learn
Docebo
Learning lab
Doctorial academy
Designing effective eLearning modules for medical education
Adaptability
Continuous assessments
Robust and continuous data collection and retrieval
Recommendation using adaptivity and adaptability
Evaluation of recommendation using knowledge tracing
Benefits of personalized e-learning systems
Allows learners to take control
Purpose driven learning
Ensures accessibility
Allows personalized feedback
Measures effectiveness of learning intervention
Collaborative learning opportunities
Interactive and engaging content
Global access to expertise
Continuing professional development (CPD)
Challenges of personalized e-learning
Feature identification and collection
Content quality and relevance
Knowledge tracing
Continuous assessments and data collection
Updating of learner's preferences
Integration with clinical practice
Motivating instructors
Faculty development and support
Digital literacy
Summary
Chapter 8. AI tools in assessment
Introduction
Challenges of traditional assessment systems
Importance of AI in assessment
Automated grading
Predictive analytics
Natural language processing
Machine learning in assessment
Intelligent content
Virtual assistants
Adaptive learning systems
Tailored evaluations
Learning managing learning (LMS)
Automated essay scoring (AES) software
Computer-based testing (CBT) platforms
Gamification tools
Tools for formative and summative evaluation
Online portfolios
Electronic evaluation instruments
Identification of patterns
Optical Mark Recognition (OMR)
Virtual reality
Diagnostic skill assessment
Prescription writing skill assessment
Emergency response skills assessment
Large language AI models and their role in classroom assessment
Test purpose determination/specification
Developing test blueprint
Test item generation/development
Preparation of test instruction
Item assembly/selection
Test administration
Test scoring
Interpretation of test results
Test analysis/appraisal
Reporting
AI assessment tools
ChatGPT
Grade scope
Role of teachers in AI-based assessment
Creating an evaluation
Giving background information
Interpreting the findings
Constant enhancement
Moral implications
Giving remarks
Individual conversation
Tracking developments
Encouraging critical thought
Enhancing accuracy
Benefits of AI in assessment
Feasibility
Automated assessment construction
AI-assisted peer assessment
Writing analytics
Continuous assessment
Incorporation of multimedia by EAP
From uniform to adaptive
Formative and summative assessment
Challenges of using AI powered tools in educational assessment
Stakeholders are not involved in the development of AI tools
Lack of transparency
Inaccurate assessment
Insufficient interpersonal contact
Limited range of application
Moral issues
Limited understanding
Compatibility with existing systems
Financial concerns
Resistance to change
The engagement and motivation of students
Establishment of standards
Technical difficulties
Comments and support
Summary
Part 3. AI in healthcare research
Introduction
Chapter 9. AI in clinical research: Transforming methodologies and discoveries
Introduction
Types of Medical research
Primary medical research
Primary medical research
Basic medical research
Clinical research
Epidemiological research
Secondary research
Meta-analysis
Systematic review
Clinical trials
Stages of clinical trial
Current challenges in clinical research
Process
Personal interest in conducting clinical research
Dedicated time allocation
Clinical data management
Role of AI technologies in clinical research
Machine learning
Study design
Study setup
Trial management
Clinical trial data management
Role of natural language processing (NLP)
Optical character recognition
ChatGPT in medical research
Applications of AI technologies in clinical research
Analysis and interpretation of data
Predictive analytics
Drug development and discovery
Optimization of clinical trials
Personalized/precision medicine
Identify trial sites
Enhance protocol architecture
Enhancing clinical design
Testing the effectiveness and safety of drugs
Data management
Recruiting and managing patients
Case report forms
Digital twin model
Medical imaging analysis
AI in genomics
Challenges of using AI in clinical research
Data quality
Data interpretability
Ethical issues
Privacy of data
Anomalous training data
A lack of transparency
Inaccurate liability
Legal issues arising in AI
Both effectiveness and safety
Privacy and data protection
Integration of AI with existing systems
Data bias
AI has technical limitations
Summary
Chapter 10. Data science and AI techniques in healthcare research
Introduction
Sources of healthcare data
Traditional ways of handing healthcare data
Ineffective care
Missed possibilities
Delayed care
Patient dissatisfaction
Cost effectiveness
Accessibility of medical records
Data science
Data components
Solid foundation in mathematics and statistics
Programming proficiency
Data preprocessing
Machine learning
Data knowledge and communication
Big data
Dimensions of big data
Volume
Variety
Value
Veracity
Velocity
Applications of big data in healthcare
Clinical data science
Clinical data management
Data mining
Healthcare data mining
Data analysis
Data analysis steps
Research question
Data collection
Data cleaning
Analyzing the data
Data interpretation and visualization
Presenting the data
Analytics for healthcare data
Descriptive analytics
Diagnostic analysis
Predictive analysis
Prescriptive analytics
Healthcare data analytics technologies
Artificially intelligent tools
Cloud computing platforms
Blockchain networks
Health information exchange (HIE)
Machine learning models
The Internet of Things
Applications of data analytics in healthcare
Healthcare research
Raising the standard of care
Drug research
Electronic health records
Administration and management of health care
Medical data privacy and fraud detection
Mental health
Public health
Pharmacovigilance
Telemedicine
Early detection of chronic diseases
Benefits of data science in healthcare sector
Minimizes treatment failure
Aids in the development of drugs
Removes human errors
Reduces health care costs
Forecasts epidemics
Provides tailored care services
Improvising operations and health administrations
Challenges of data science in the healthcare industry
Data structure issues
Missing data and data sparsity
Security issues
Data standardization issues
Data irregularity
Biases in data
Data storage and transfers
Summary
Chapter 11. AI-driven drug discovery and development
Introduction
Stages of drug discovery and development
Prediscovery phase
Preclinical phase
Target identification and validation
High-throughput screening
Assay development and screening
Hit to lead
Lead generation and optimization
In vivo and in vitro assays
Clinical research
Phase I trials
Phase II trials
Proof of concept trials
Phase III trials
Phase IV trials
FDA review
FDA postmarket safety monitoring
Traditional system of drug discovery
Advantages
Disadvantages
Protracted process
Exorbitant costs
Human limitations
Data bases and tools for drug development
PubChem
ChEMBL
DrugBank
UniProt database
Protein data bank
AI in drug discovery process
AI technologies in drug discovery
Machine learning
Deep learning
High-throughput density functional theory
Natural language processing (NLP)
Text mining
Generative adversarial networks (GANs)
Variational auto-encoder
Support vector machine
Recurrent neural network
Generative AI in drug discovery
Quantum AI: The new era of drug discovery
AI-based software tools for drug development process
AlphaFold2
DeepChem
DeeperBind
Deep Affinity
Role of AI in drug discovery and development
De novo drug design
Accelerated drug development
Improved clinical trial design
Quality assurance
Target structure prediction
Target identification and validation in drug
Lead discovery procedure
Lead optimization procedure
Virtual screening (VS) and optimization of compounds
Preclinical and clinical development
Drug repurposing
FDA approval and postmarket analysis
Drug combination analysis
AI for drug discovery monitor post market safety
Challenges and limitations of AI assisted drug discovery
Lack of transparency
Smaller datasets
Complex data
Lack of high-quality data
Excessive reliance on data
Ethics and regulatory concerns
Lack of standardization
Summary
Chapter 12. Predictive analytics and AI in epidemiological studies
Introduction
Predictive analytic process
Goal of prediction
Data gathering and cleansing
Data analysis
Predictive modelling
Training the model
Validation and testing
Prediction and deployment
Continuous improvement
AI techniques in healthcare predictive analytics
Machine learning
Supervised learning
Unsupervised learning
Deep learning
Natural language processing
Computer vision
Reinforcement learning
Predictive analytics and AI in epidemiology
Application of predictive analysis in healthcare
Clinical predictions
Personalized treatment plans
Epidemic outbreak prediction
Diagnostic imaging
Managing population health
Determining chronic illnesses
Determining the spread of diseases
Submission of insurance claims
Value based care
Predicting patient behavior in the hospital
Management of patient flow
Observation of patients
Patient satisfaction
Patient involvement
Analyzing equipment maintenance requirements
Preventing patient deterioration in ICUs and general hospitals
Suicide attempt prediction
Improving patient engagement
Minimizing missed appointments
Predictive analytics prevent patient's readmission
Resource allocations and optimization of the supply chain
Risk stratification
Benefits of predictive analytics in healthcare
Challenges of using AI in predictive analytics (Fig. 12.5)
Data quality and quantity
Overfitting and underfitting
Model interpretability
Changing data distributions
Ethical and bias concerns
Feature selection and engineering
Optimal model selection
Limited domain expertise
Deployment and integration
Continuous monitoring and maintenance
Summary
Part 4. AI in clinical practice
Introduction
Chapter 13. AI in diagnostics: Enhancing accuracy and efficiency
Introduction
Drawbacks of the traditional methods of diagnosis
AI technologies in medical diagnosis
Machine learning technology in diagnostic medicine
Machine-learning-based disease diagnosis (MLBDD)
The potential benefits of ML diagnostic technologies
Early detection
Consistency
Access
Challenges affecting ML technologies for medical diagnostics
Demonstrating real-world performance
Fulfilling health needs
Addressing regulatory gaps
Deep learning in medical diagnostics
Convolution neural network (CNN)
Computer vision (CV)
Medical imaging
Identifying objects
Objects detection
Semantic segmentation
Instance segmentation
Cardiology
Natural language processing (NLP)
Explainable AI
Clinical decision support systems
Quantum AI
Generative AI and self-diagnosis
AI tools used for diagnosis
Role of AI in medical diagnosis
Clinical diagnosis
AI in differential diagnosis
AI in oncology
AI in cardiology
AI in psychiatry
AI in dermatology
AI in genetics and genomics
AI in pathology
Medical imaging and diagnostic services
Segmentation and detection
Image enhancement and reconstruction
Cardiovascular diseases
Respiratory conditions
Central nervous system
Gastroenterology
Ophthalmology
Virtual patient care
Personalized medicine
Robotic technology in diagnosis
Predicting outbreaks
Benefits of AI in healthcare diagnostics
Precision and accuracy
Quicker response time
Prompt identification
Tailored approach to therapy
Enhancing outcomes
Cost effectiveness
Remote diagnostic capabilities
Challenges of using AI in diagnosis
Technical challenges
Ethical concerns
Legal issues
Data quality and accessibility
Interoperability and integration with existing systems
Explainability and trust issues
Inaccurate information
Summary
Chapter 14. AI-enabled decision support systems in clinical practice
Introduction
Decision support system
Clinical decision support system
Process of medical decision making
The four step approach
Core skills in decision making
Pattern recognition
Critical thinking
Communication skills
Information provision
Evidence-based approaches
Leadership and team work
Sharing
Reflection
Factors that affect decision making
Challenges of traditional medical decision making
Need for CDSS
Elements of clinical decision support system
The data management layer or base
An inference engine
User interface
The decision-making process
Classification of CDSS
Based on the timing of intervention
Prediagnosis CDSS
Postdiagnosis CDSS
Depending on the approach used to give advice
Passive CDSS
Active CDSS
Depending on the use of knowledge CDSS can be classified into
Knowledge based clinical decision support system
Non-knowledge-based clinical decision support systems
Based on the analytical capabilities
Predictive CDSS
Descriptive CDSS
Prescriptive CDSS
AI technologies in CDSS
Role of machine learning in clinical decision support systems
Neural networks
Support vector machines
Random forests and decision trees
Natural language processing
Deep learning
Intelligent decision support system
Functions of CDSS
Integrate information
Risk prediction and management
Patient safety
Clinical management
Cost containment
Order entry requirement
Diagnostics support
Treatment recommendations
Monitoring and feedback
Alert and reminders
Personal health records
Chronic diseases
Education and training
Benefits of AI clinical decision support systems
Enhancing diagnostic accuracy
Making more informed decisions
Helping and assisting physicians
Reduced errors
Standardized care
Consistent information delivery
Quality of decision making
Challenges of CDSS
Fragmented workflows
Alert fatigue and inappropriate alerts
Impact on user skill
CDSS may be dependent on computer literacy
System and content maintenance
Operational impact of poor data quality and incorrect content
Lack of transportability and interoperability
Financial challenges
Summary
Chapter 15. Robotics and AI-assisted surgery: Advancements and applications
Introduction
Medical robotics
Surgical robots/robot-assisted surgery
Robotics for radiotherapy
Hospital robots
Laboratory robots
Rehabilitation robots
Robotic prosthetics
Social robots
Exoskeleton robots
Surgical robots
Robotic-assisted surgery system
Patient cart
Surgeon console
Vision chart
Common surgical robots
The da Vinci surgical robot
The CyberKnife
The Xenex Germ-Zapping robot
The PARO therapeutic robot
TUG
Advantages of robotic-assisted surgery system
Staunch friend
Advantages for the patient
Benefits of robotic surgeries
Challenges of robotic surgeries (Fig. 15.3)
Precision and control
Real-time imaging and navigation
Training and skill acquisition
System reliability and maintenance
Cost and accessibility
AI in robotic surgery
Challenges of traditional surgical techniques
Disadvantages of traditional surgery
Big incisions
Prolonged recovery period
Pain and discomfort
Blood loss
Scarring
Lengthier hospital stays
Risk of complications
Extended anesthetic exposure
Surgical procedure complexity
Factors connected to the surgeon
Surgical burnout
AI technologies in surgery
Machine learning
Natural language processing
Artificial neural networks
Computer vision
Augmented reality
Application of AI in surgery
Preoperative phase
Intraoperative phase
Postoperative phase
AI in surgical training
Role of AI in robotic autonomy
AI in robotic surgical assessment and feedback
Examples of AI-assisted surgery
Benefits of using AI in surgery (Fig. 15.6)
Support surgical decision-making
Boost surgical diagnostics
Streamline workflow
Enhances patient safety
Reduce personnel
Better results for patients
Better hospital administration
Challenges in artificial intelligence assisted surgery
Advancements of robotics in the medical field
Improved human touch
Better robotic performance in surgeries
Increasing the empathy for robots
Tele-nursing
Future perspectives
Improved operations scheduling using automated analysis
Capacity to prevent adverse events during operation
Improved recovery rates by post-operative surveillance
Collaboration
Summary
Chapter 16. AI-driven personalized health care
Introduction
Sources of personal data
Clinical history
Lab investigation
Genomic data
Electronic health records
Insurance claim
Feedback forms
Home testing kits
Wearable devices
Lifestyle and environmental data
Personalized healthcare
Components of personalized healthcare
Individualized medicine
Precision medicine
P4 medicine (predictive, preventive, personalized, and participatory)
Stratified medicine
Personalized medicine
Precision medicine versus personalized medicine
Precision medicine
Personalized medicine
Need for personalized medicine
Multifaceted approach of personalized healthcare
Assessment of risk
Modifiable risk factor management
Early detection of disease
Accuracy of diagnosis
Best possible treatment
Effective management
Importance of the personalized health care
For the patients
For the healthcare providers
AI technologies in personalized healthcare/personalized medicine
Machine learning
Data integration and analysis
Deep learning
Predictive analytics
Real-time monitoring
Image recognition and diagnosis
Generative AI
Companies delivering AI driven personalized healthcare
Tempus
HealthJoy
Paige.AI
Babylon health
Komodo Health
Examples of personalized healthcare
Genomics and cancer
Personalizing disease prevention
Therapeutics
Chronic diseases
Obstetrics and gynecology
Applications of AI in personalized medicine
Personalization of cancer therapy
Cardiovascular disease management
Neurological disorders and mental health
Benefits of AI-driven personalized healthcare
Challenges of AI in personalized medicine
Data quality
Privacy concerns
Ethical implications
Cost and accessibility
Regulatory challenges
Summary
Part 5. Future directions and challenges
Introduction
Chapter 17. AI in telemedicine and remote patient monitoring
Section A: Telemedicine
Introduction
History
Types of telemedicine
Real-time interactive telemedicine
Store-and-forward telemedicine (asynchronous video)
Remote patient monitoring (telemonitoring)
Components of telemedicine
Importance of telemedicine
Applications of telemedicine
Teleconsultation
Remote psychotherapy
Remote imaging
Telepathology
Patient care in emergencies
Specialized counseling system
Telemedicine and robotics
Telemedicine in pediatrics
Integration of AI into telemedicine
Applications of AI tools in telemedicine
Teleophthalmology and AI
Tele stroke and AI
Tele dermatology and AI
Management of renal diseases
Management of cardiac diseases
Smart patient monitoring
Automating administrative workflow
Benefits of integrating AI in telemedicine
AI-driven diagnostics: Facilitating accurate remote assessment
Extended reach of medical services
Speeding up the treatment process
Development of personalized treatment plans
Management of chronic conditions
Elderly care
Home care
Patient safety
Challenges of telemedicine
Cost of system development
System implementation
Digital literacy
Digital technology acceptance
Diagnostic accuracy
Privacy
Lack of technology
Lack of knowledge and training
Section B: Remote patient monitoring
Introduction
Architecture of RPM
Data acquisition and processing
Data transmission
End user
Remote patient monitoring devices
Continuous glucose monitors (CGM)
Digital blood pressure monitors
Holter-e patch
Heart rate sensor
Pulse oximeter
Wearables (activity trackers and continuous monitoring)
Weighing scale
AI in RPM applications
Enhanced data collection and analysis
Personalized care and treatment
Data analysis and pattern recognition
Personalized treatment plans
Adaptive monitoring
Behavioral insights
Patient education and engagement
Continuous learning and improvement
Remote diagnostics and telemedicine
AI and interoperability of data
Image and signal analysis
Reducing diagnostic errors
Remote consultations and telemedicine platforms
Continuous monitoring
RPM software solutions
Data analysis and insights
Predictive analytics
Remote diagnostics
Virtual triage and symptom assessment
Behavioral pattern recognition
Language processing for telemedicine
Improved patient engagement and self-management
Personalized health recommendations
Virtual health assistants and chatbots
Behavioral analysis and feedback
Incentives and gamification
Enhanced medication adherence
Emotional support and mental health
Benefits of AI in RPM
Enhanced patient outcomes
Increased efficiency for healthcare providers
Personalized care
Effectiveness of operations
Empowering patients
Cost-effectiveness
Challenges of AI in RPM
AI or ML explainability
Privacy
Uncertainty
Signal processing
Imbalanced dataset
Dataset volume
Summary
Chapter 18. Emerging trends in AI in healthcare: Innovations and opportunities
Introduction
Emerging trends of AI in healthcare
Patient-centric technologies
Internet of medical things (IoMT)
Blockchain technology
Remote patient monitoring
Point-of-care testing
Emergence of VR and AR
3D printing and implants on healthcare
Cloud computing
Nanotechnology
Geriatric care
Virtual health assistants and chatbots
Enhancing social determinants of health
Innovations of AI in healthcare
Generative AI platforms
Medical imaging
Drug discovery and development
Medical research and data analysis
Personalized medicine
Multimodal large learning models for clinicians
AI in blood testing
Evidence based digital therapeutics
AI-powered medical devices
AI digital twins
Autonomous AI
Era of Super AI
Conversational AI
Making appointments
Care management for patients
Assistance for patients
Smooth bot to agent transfer
Innovations in medical education
Streamlining learning processes with AI
Enhancing surgical training
Opportunities of AI in healthcare
Technological advancements
Diagnosis and patient monitoring
Drug discovery and development
Virtual health assistants
Participatory health
Unified healthcare system
Collaboration and making choices
Improved staff-patient contact in healthcare
Diagnosis and planning for treatment
Simplifying tasks in administration
Summary
Chapter 19. Challenges and limitations of AI adoption in healthcare
Introduction
Challenges of AI in healthcare
Data challenges
Data availability
Privacy and security
Quality of data
Methodological research flaws
Accuracy of data
The importance of interoperability
Technology development
Bias
Data breach
Blackbox
Implementation
Existing laws and policies
Nonparticipation of the stake holders
Building trust for AI systems acceptance in clinical practice
The widening skills gap
Financial barriers to successful implementation
Patient preparation for new methods
Ethical factors
Privacy
Health Insurance Portability and Accountability Act (1996)
HITECH
General Data Protection Regulation (2018) (EU)
Safety
Transparency and accountability
Regulation
Social factors
Trust
Myths about AI
Overestimation
Challenges of using AI in medical education
Academic dishonesty
Academic dishonesty in clinical research
Impact of dishonest practices on research credibility
Role of AI in academic dishonesty
Role in academic writing
Automatic Article Generator
Generative AI
Proactive solutions to academic dishonesty
Administration
Educator
Educating the student
Role of AI detection software
Summary
Chapter 20. Training the future healthcare workforce for AI integration
Introduction
Current status of healthcare education and clinical practice
Lack of emphasis on technological literacy
Insufficient integration of AI and machine learning
Insufficient expertise
Continuous updating
Cooperation and multidisciplinary proficiency
Need for preparing the healthcare workforce
Frameworks for healthcare professional training
Integrating artificial learning into healthcare curricula
Enhancement of curriculum
Practical experience
Multidisciplinary method
Lifelong learning
Role of university in spreading awareness about updated knowledge
Role of committees
Student-driven learning with advanced technology
Individualized, active learning
Social interaction
Resource accessibility
Real-time feedback and continuous improvement
Designing a course targeted toward health workforce
Foundational AI education for all healthcare workers
Advanced AI education for specific workforce
Training of healthcare workers
Areas of concern
Administrative burden
An excessive workload and burnout
Taking care of and monitoring patients in intensive care units
Diagnostic accuracy
Benefits of training the future healthcare workers in AI
Challenges of using AI training the future workforce
A change in the viewpoint of the health workforce
Investment of money
Interprofessional collaboration
Ongoing education
Inadequate background
Formulating an integrated course of study
Summary
Part 6. Practical application of AI tools in healthcare
Introduction
Chapter 21. Practical training of AI tools in medical education
Introduction
Current scenario in practical teaching
Benefits of practical learning in medical education
Enhanced capabilities
Enhanced knowledge
Empowering effect
Increased retention of information
Challenges to the traditional practical sessions (Fig. 21.1)
Lack of time
Insufficient adaptability
Inadequate infrastructure
Rote learning
Importance of hands-on training in medical students
Observing protocols
Real-time and precise feedback
Possibilities to connect
Interprofessional collaboration
Continuing medical education
Impact of AI on practical medical education
Practical AI tools used in hands-on training of medical students
Virtual reality
Augmented reality
Computer vision
Integrated simulations
Personal digital assistants
AI avatars
Remote patient monitoring and smart home-based health platforms
Smart home for healthcare and health informatics training laboratories
Chatbots
Intelligent tutoring system
Virtual patients
Gamification
Adaptive learning system
Teaching-specific medicine specialties
Incorporation of AI into practical sessions
Practical sessions
AI body
Organizing workshops
Undergraduate research
Application of generative AI technologies
Internships and mentorships
Use social media sites to interact with students
Utilize virtual reality and augmented reality to offer training opportunities
Create simulations for experiential learning
Summary
Chapter 22. Practical training of AI tools in clinical research
Introduction
The importance of clinical research
Importance of clinical research for medical students
Benefits of clinical research
For students
Practical experience
Develop critical thinking and problem-solving ability
Exposure to advanced technologies
For clinicians
Reaching patient recruitment targets more quickly
Establishing better patient connections and health outcomes
Increasing underserved and/or rural communities' access to healthcare
For aspiring researchers
Moral considerations
Research methodology
Gathering and analyzing data
Rules and regulations
Multidisciplinary cooperation
Comprehending research techniques
Acquiring technical knowledge
Expanding professional prospects
Traditional versus modern research methods
Technology and information gathering
Multidisciplinary method
Unrestricted access and cooperation
Reliability and strictness
Diversity and globalization
Moral aspects to take into account
Emphasize the impact in the real world
Quick transmission
Automation and artificial intelligence
Prioritization of sustainability
Importance of practical training in clinical research
AI in clinical research
Practical AI tools in clinical research
Google Scholar
Scite
Trinka
IBM Watson
Tableau
Semantic Scholar
Applications of AI-based tools in clinical research
Literature review tools
Knowledge maps tools
Connected papers
Research Rabbit
Tools for reading papers
Enago read
The Scispace
Tools for making notes
Glasp
Lateral AI
Tools for writing papers
PaperPal
Jenni AI
Data analysis tools
Methods to train the faculty in the usage of AI tools
Conduction of regular workshops
Hands on experience
Advantages of using AI tools
Challenges of using AI tools in research
A lack of transparency
Bias in data
Privacy issues
Restricted human engagement
Summary
Chapter 23. Practical training of AI tools in healthcare work force
Introduction
Importance of practical training for clinicians
Benefits of gaining practical experience during medical school
Improved patient care skills
Increased confidence and competency as a medical professional
Enhanced ability to apply knowledge to real-world situations
Improved communication skills
Traditional versus E-learning in clinical medicine
The pros of traditional learning
Cons of traditional teaching
AI in healthcare delivery
Preventive care
Diagnosis and treatment
Drug discovery
Surgical procedures
Patient care and communication
Telemedicine and remote patient monitoring
Administrative tasks
Areas of importance
AI technologies
Epidemiology
Statistics
Drug discovery methods
Clinical diagnostics
Robotics
Smart medical devices
Telehealth and remote care
Connected emergency response solutions
Smart hospital management
The Internet of Things
Rehabilitative care
Clinical decision support systems
Practical AI tools in hands on training of clinicians
AI-integrated preclinical medical education
AI-integrated resident training
AI-integrated training for attending physician/consultant
MedBridge
Osmosis
Touch Surgery
Prognos Health
Arterys
MyoStrain
Daisy intelligence
DynaMed
Healthcare professionals training
Hands-on experience with AI tools and platforms
Tool familiarization
Simulation and virtual training environments
Encouraging interdisciplinary collaboration and knowledge-sharing
Conduction of interprofessional workshops
Taking online courses
Training programs
Social media
Advantages of AI using tools in physician training
Decreasing cost of treatment
Increased precision and effectiveness
Tasks related to administration
Accurate decision-making
Quality of care
Time saving
Easy information sharing
Challenges of using AI tools in physician training
Summary
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Fundamentals of AI for Medical Education, Research and Practice

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Sameer Mohommed Khan

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