Artificial Intelligence in Medicine

Foreword to Artificial Intelligence in Medicine
Quotation
Preface
Acknowledgments
In Memoriam
Contents
About the Editors
Contributors
Part I:
	1 Basic Concepts of Artificial Intelligence: Primed for Clinicians
		Introduction
			AI, Machine Learning, and Deep Learning per Definition
			A Brief History of AI
			Rising Demand for AI
			AI Applications
			AI Staging
		AI Programming Languages
			Machine Learning
			Types of Machine Learning
			Machine Learning Problem Solutions
				Supervised Learning Algorithms
				Unsupervised Learning Algorithms
				Reinforcement Learning
			Limitations of Machine Learning
			Introduction of Deep Learning
			Single-Layer Perceptrons
				Multilayer Perceptron - Artificial Neural Network
			Natural Language Processing
		Conclusions
		References
	2 Applying Principles from Medicine Back to Artificial Intelligence
		Introduction
		Concepts from the Medical Sciences Being Applied Back to the Field of Artificial Intelligence: Neural Networks
		Concepts from the Medical Sciences Being Applied Back to the Field of Artificial Intelligence: Cognitive Architectures
		Concepts from the Medical Sciences Being Applied Back to the Field of Artificial Intelligence: The Causal Cognitive Architectu...
		Discussion
		References
	3 Mathematical Foundations of AIM
		Introduction
		Classical Machine Learning and Its Limitations
			The Perceptron Model
			Support Vector Machine
		Modern Deep Learning Revolution
			Architectures of Modern Deep Neural Networks
			Representation Power of Deep Neural Networks
			Other Properties of Deep Neural Networks
		Algorithm Unrolling: From Iterative Algorithms to Deep Networks
			Learned Iterative Shrinkage and Thresholding Algorithm
			Unrolling Generic Iterative Algorithms
		Interpretations of Deep Learning
			Hierarchical Features in the Visual System
			Geometric Understanding of Deep Neural Networks
		Summary and Outlook
		References
Part II:
	4 Introductory Approaches for Applying Artificial Intelligence in Clinical Medicine
		Introduction
		Short AIM History
		Electronic Health Records (EHRs) and AIM Interaction
		Learning Health Systems
		Some Industrial Cases - An Overview
		AIM Applications
		Financial Aspects of AIM
		Emerging Markets
		Clinical Decision Support Systems
		Conclusion
		References
	5 Introduction to Artificial Intelligence in Medicine
		Introduction
		The Development of the AI Framework
		How Does a Convolutional Neural Network Work?
			Convolution and Cross-Correlation
			A Close Look into ``AlexNet´´
				Forward Pass
				Backward Pass
		Representation Learning
			Unsupervised Learning and a Geometric Model
		Network Topologies, Types of Learning and Performance Measures
			Topologies of Networks
			Types of Learning
			Measures of Performance: Sensitivity, Specificity, ROC
			Inference and Network Examples
		Deep Neural Network Application Domains
		Relation to the Visual System
			Color Processing and Colorization
			Foveated Vision
		Discussion
			Lessons for All Doctors
		References
	6 Importance of AI in Medicine
		Introduction
		The Amazing World of AI
		A World of Terminology
		Neural Nets: Quite Similar to Neural Networks in the Human Brain
		Data: The Fuel for AI
		Segmentation: Feeding Quality Data
		Training Mode
		Multisensor Data
		Prediction Mode
		Levels of Expertise
		Self-Evaluation Mode and Correlations
		Breaking Boundary Conditions
		Multiple Ways of Solving a Problem
		Processing Improvements
		Validation
		Gatekeepers
		Limitations
		Responsible Artificial Intelligence
		Various Agendas
		Transparency
		When to Use AI in Medicine
		References
	7 The New Frontiers of AI in Medicine
		Introduction
		Natural Language Processing
			Can Future AI Systems Use Natural Language Processing to Improve How Individuals Engage in Their Health?
			Unlocking Clinical Knowledge to Address Common Patient Concerns Through Clinical Avatars
			Engineering the Next-Generation Electronic Health Record and Improving Staff Workflows
		Machine Learning, Deep Learning, and Neural Networks
			Improved Disease Detection Through Computationally Enabled Diagnostics
			Entirely New Ways of Diagnosing Disease Using Digital Biomarkers
			AI-Driven Population-Level Interventions for Entire System Planning and Optimization
		Computer Vision
			Using AI to Generate Real-Time Synthetic Images to Improve Surgical Interventions
			AI-Driven Surgical Success Measures to Create Intelligent System-Wide Resource Planning
		Robotics
			Partly Automated Surgery for Rapid Recovery and Safety
		New Frontiers in AI
			AI Bias Gives Another Perspective on Driving Improvement
				Healthcare Service Redesign
			Evidence-Based Individualized Treatment Pathways
		The Future of AI in Medicine
		References
	8 Social and Legal Considerations for Artificial Intelligence in Medicine
		Introduction
		Social Challenges
		Juristic Challenges
			Tort Law
		Conclusions and Guidelines
		References
	9 Ethical Challenges of Integrating AI into Healthcare
		Respect for Autonomy
		Beneficence
		Nonmaleficence
			Privacy
			Safety
		Justice
		Impact on the Physician-Patient Relationship
		References
	10 Artificial Intelligence in Medicine and Privacy Preservation
		Introduction
		General Considerations and Current Technical Standards
			Anonymization, Pseudonymization, and k-Anonymity
			Considerations for Specific Dataset Types
			The Requirement for Next-Generation Privacy-Preserving Techniques
		Federated Learning
			Technical Framework
			Challenges in Federated Learning
			Attacks Against Federated Learning Systems
			Applications of Federated Learning
		Differential Privacy
			Properties
			Implementation
			Sensitivity and Privacy Budget
			Challenges
			Applications
		Homomorphic Encryption and Secure Multi-Party Computation
			Homomorphic Encryption
				Applications
			Secure Multi-Party Computation
				Applications
		Trusted Execution Environments
		Outlook
		References
	11 Artificial Intelligence for Medical Decisions
		Introduction
			Automation of Decision-Making in Healthcare
			Taxonomy of Medical Decisions
		Logic-Based Methods
			The Language of Logic
			Knowledge Representation and Reasoning
			Beyond First-Order Logic
		Learning from Data
			Statistical Modelling and Machine Learning
			Three Machine-Learning Approaches
			The Impact of the Deep Learning Revolution
		Combinatorial Optimization Methods
			Reinforcement Learning for Sequential Decision-Making
		Bayesian Models for Decision Support
			Bayesian Networks for CDSS
		The Need for Causality in Clinical Decision-Making
		Explainability, Interpretability, and Fairness
		Conclusion
		References
	12 Artificial Intelligence for Medical Diagnosis
		Introduction
			Diagnostic Reasoning
		Knowledge-Based Diagnosis
			Rule-Based Diagnosis
			Fuzzy-Logic Systems
			Ontology-Based Systems
		Model-Based Diagnosis
			Abductive Diagnosis
			Bayesian Diagnosis
			Causal Reasoning for Diagnosis
		Machine Learning for Diagnosis
			Learning from Data
			Machine Learning as Function Approximation
			Three Supervised Methods
			Other Machine Learning Formalisms for Diagnosis
			The Importance of Data
		Outlook
		References
	13 AIM and the History of Medicine
		Introduction to the History of Cognitive Science and Intelligence
		Mechanical and Biological Automatons
			The Golem
			The Ars Magna
		The Concept of Symbolic Languages
			Hurufism
			The Calculator
		The Technological Myth Persists
		AI is on its Way
		Conceptual Revolutions
		World, Meet the Personal Computer
		The Big Question: Can Computers Think?
		Earliest Steps towards Computerized Medicine
		Collaboration Between Medical Scientists and AI Researchers
			Examples of the Use of AI in Medicine
		Artificial and Biological Life
		Conclusion
		References
	14 AIM and Patient Safety
		Introduction
		Trends in Quality and Safety Research
		Approaches to Patient Safety and Preventing Errors
		Intelligent Systems: Machine Learning and Natural Language Processing
			Prevention of Adverse Events
			Diagnostics
			Medication Errors and Polypharmacy
			Treatment Outcomes and Quality
		Patient Safety Databases
		Future of AI in Safety and Quality
		References
	15 Right to Contest AI Diagnostics
		Introduction
		The Right to Effective Contestability and Transparency
		The Four Dimensions of Contestability
			Dimension 1: Personal Health Data
			Dimension 2: Bias
			Dimension 3: Performance
			Dimension 4: Decisional Role
		Further Issues and Aspects of the Right to Contest
		References
	16 AIM in Medical Informatics
		Introduction
		Clinical Data
			Electronic Health Record
			Omics Data
			Diagnostic-Related Information and Treatment Information
		Data Processing
			Missing Value Imputation
			Dimensionality Reduction
			Different Omics Processing
		Existing and Emerging Applications in Medical Diagnosis
			Clinical Data Application
			Omics Data Application
		Explainability of Results
		Future Perspectives
		Conclusion
		References
	17 Artificial Intelligence in Evidence-Based Medicine
		Introduction
		From PICO Questions to Systematic Reviews
		Automation of Systematic Reviews
			Development of Search Strategies
			Screening
			Data Extraction
		New Types of Evidence
			Making Data More FAIR
			Other Sources of Data
		Improving Shared Decision-Making
		Summary
		Cross-References
		References
	18 AIM in Electronic Health Records (EHRs)
		Introduction
		The Symbolic-Connectionism Cognitive Architecture
		The Holistic Framework of Research Work of WI Lab in AIM
			Data Tier
			Knowledge Tier
			Application Tier
				Disease Diagnosis
				EMR Quality Control
				Overtesting Detection
				Disease Risk Prediction
				Chronic Disease Management
				Dermatosis Diagnosis
		Selected Research
			Corpus Construction
			Information Extraction
			Knowledge Expansion
				Integrating External Knowledge
				Mining Potential Knowledge
			Clinical Reasoning Model
				Knowledge Representation
				Disease Diagnosis
			Disease Risk Prediction
		Conclusion and Future Work
		References
	19 AIM and Causality for Precision and Value-Based Healthcare
		Current Standard of Care and Medical Research
		Current AI Practice in Medicine and Healthcare
		Model-Driven AI and Causal Diagnostics
		The Opportunity for Precision Medicine
		Conclusions
		References
	20 AIM and the Nexus of Security and Technology
		Introduction
		Human Perfection and the Security Implications of Biomedical Technology
			Eugenics: The Painful History of an Idea
			Can ``Liberal Eugenics´´ Really Work? The Relationship Between New Wave Eugenics and Security
		Exploring the Symbiotic Relationship Between Technology and Security
			Hacking Healthcare: The Violation of Patient Data
		Concluding Thoughts
		References
			Bibliography
	21 AIM in Unsupervised Data Mining
		Introduction
		Association Rule Mining
			FRM
			BRM
		Likelihood Mining Criterion (LMC)
			LMC-FRM Comparison
		Basic Rule Mining Example
			Methodology
			Evaluation
		Census and Chemical Exposure Database Mining
			Methodology
			Evaluation
		Rehabilitation Routine Mining
			Methodology
			Evaluation
		Conclusions
		References
	22 AIM in Medical Education
		Introduction
		AI in Various Specialty Delivered Medical Education
			Literature Review
				Inclusion and Exclusion Criteria
					Meta-Analysis
					Bias Assessment
					Results
					Results from Screening
					Bias Assessment Result
					Meta-Analysis
			Discussion
			Meta-Analysis
				Proposing a New Method of Unbiased Reporting of Meta-Analysis of Improvement in AI Studies
				The State of Medical and Surgical Training and the Use of AI in Simulation
				AI in Medical Specialties for Education
				AI in Ophthalmology, Dermatology, Cardiology, Gastroenterology, and Rheumatology
				AI in Obstetrics and Gynecology, General Surgery, Orthopedic, and Neurosurgery Education
				AI for Surgical Performance Assessment
				AI for Precision Examination and Ethico-Legal Aspects of Medical Education and Autonomous Robotic Surgical Regulation
		Conclusion
		References
	23 AIM and Evolutionary Theory
		Introduction
		Mathematical Oncology
			Prediction of Cancer Risk
			Precision Medicine
			Future Directions
		Infectious Disease
			Drug Resistance
			Drug Design
			Emerging Pathogens
		Avoiding the Desynthesis
		References
	24 AIM and the Patient´s Perspective
		Introduction
		AIM and the Patient´s Perspective
			The Need for Transparency
			Regulations and Data Sharing
			The Public´s Understanding of AI
			The Public´s View on Data Sharing
			Weighing the Benefits and Risks
			Levelling the Playing Field
		Gaining the Public´s Trust
			Technologies for Trust
				Privacy by Design
				Blockchain
				Federated Computing
				Dynamic Consent
		Conclusion
		References
	25 AIM and Hackathon Events
		Introduction
		Organizing AIM Hackathons
			Proposed Metrics for Measuring the Success of an AIM Hackathon
			Limitations of the Hackathon Approach
			Recommendations for Organizing Better Hackathons
		References
			Further Reading
	26 AIM, Philosophy, and Ethics
		Introduction
		Promises of AI in Medicine
		AI and Medical Epistemology: A Changing Paradigm
			Data
			Data-Utopianism
			Data Curation and Use
		AI and Medical Epistemology: Limits, Risks, and Biases
			Human Biases and Prejudices: Language and Interpretation
			Computational Biases: Programming and Algorithms
		AI and Medical Ethics
			The Patient-Doctor Relationship
			The Medical Profession in the Era of Digital Capitalism
		Conclusion and Recommendations
		References
	27 Reporting Standards and Quality Assessment Tools in Artificial Intelligence–Centered Healthcare Research
		Introduction
		The Case for AI-Specific Instruments
		Specific AI Reporting Standards
			SPIRIT-AI and CONSORT-AI
			STARD-AI
			TRIPOD-AI
		Specific AI Quality Assessment Tools
			QUADAS-AI
			PROBAST-AI
		Conclusion
		References
	28 AIM and Gender Aspects
		Introduction
		Sex and Gender Differences in Medicine
		Sex and Gender Bias in Machine Learning Models
		Role of Sex and Gender in Machine Learning Models for Medicine
			Current Issues
			Outlook and Potential Solutions
				Collecting Balanced Datasets
				Disaggregating Data
				Data and Model Documentation
				Fairness Aware Models
				External Auditing Framework for Machine Learning Models
				Continuous Monitoring of the Models
		References
	29 Meta Learning and the AI Learning Process
		Introduction
		General Considerations
		Transfer Learning
		Few-Shot Learning
			Continual Learning
		Multi-task Learning
		Neural Architecture Search
		Conclusion
		References
	30 Artificial Intelligence in Medicine Using Quantum Computing in the Future of Healthcare
		Introduction
		History
		The Basics of Quantum Computingand Quantum Machine Learning
			Types of Quantum Computers
			Companies
		Basic Anatomy of a Quantum Versus Classical Computing Circuit
			The Bit and the Classical Logic Gate
			The Qubit and the Quantum Gate [58]
			State Vectors
			The Quantum Gates [58]
		Quantum Superposition and Machine Learning Applications
			Theoretical Medical Applications
		Quantum Tunnelling and Machine Learning Applications
		Quantum Entanglement and MachineLearning Applications for Medicine and Surgery
			Basic Mathematical Formalism of Entanglement Applied to Medicine [58, 74]
		Important Quantum Computing Algorithms and Quantum Machine Learning Algorithms
			Quantum Fourier Transform
			Shor´s Algorithm
			Grover´s Search Algorithm
			Deutsch-Jozsa Algorithm
			Bernstein-Vazirani Algorithm
			Quantum Hidden Markov Chain Algorithm
			Quantum Natural Language Processing
			Quantum Annealing and Quantum Neural Networks
			Quantum Enhanced Reinforcement Learning
		Quantum Phenomenon in Disease
		Quantum Computing in Healthcare
		Future Translational Considerations for E-Healthcare
			Ethico-Legal Implications and Considerations
		Summary Remarks
		References
Part III:
	31 Emergence of Deep Machine Learning in Medicine
		Introduction
		Deep Neural Networks
		The Universal Approximation Theorem and Its Limitation
			Internal Hierarchical Feature Extraction
			Internal Transformation of Dataset Topologies
		Medical Examples
		Medical Imaging
		Genomics and Epigenomics
		Natural Language Processing
		Conclusion
		References
	32 AIM in Interventional Radiology
		Introduction to Interventional Radiology
		AI in Medical Imaging: A Primer
		Data in Medical Imaging
		AI in Diagnostic Radiology: Brief Overview
		AI in Interventional Radiology: Unique Challenges
		AI in Interventional Radiology: Opportunities
		The Ideal Interventional Radiology Suite
		Scope of AI in Interventional Radiology
			AI in IR: Decision Support
			AI in IR: Triaging of Patients
			AI in IR: Prevention of Errors
			AI in IR: Periprocedural Support
			AI in IR: Patient Monitoring and Procedural Support
			AI in IR: Prognostication and Outcome Prediction
			AI in IR: Image Acquisition and Processing
			AI in IR: Residency and Fellowship Training
		Can AI Replace Diagnostic or Interventional Radiologists?
		References
	33 Automated Deep Learning for Medical Imaging
		Introduction
		Challenges of Deep Learning for Clinical Researchers
			Highly Specialized Technical Expertise
			Compute Resources
			Large, Well-Curated Datasets
			Data Protection and Privacy
		Principles
		Initial Work Utilizing Automated Deep Learning in Medicine
		Automated Deep Learning Process
		Limitations of Automated Deep Learning
			General Limitations
				Explainability
				Generalizability and Bias
			Automated Deep Learning-Specific Limitations
		Future Directions of Automated Deep Learning
			Use Cases of Automated Deep Learning
			Packaging and Deployment of These Models
		Conclusion
		References
	34 AI in Musculoskeletal Radiology
		Introduction
		Machine/Deep Learning
		Musculoskeletal (MSK) Diseases: A Rapidly Rising Global Burden
			Bringing Disease Management into the Digital Age
		Musculoskeletal Radiology
		AI in Computed Radiographs
		Building and Validating AI Models for MSK Radiographs
		Quality Control
		Anomaly Detection
		IB Lab KOALA: Assessment of the Gonarthrosis Stage
		IB Lab PANDA: Determination of Pediatric Bone Age
		IB Lab HIPPO: Measurement of the Hip and Pelvis
		IB Lab LAMA: Measurement of the Whole Leg
		AI Disrupts Status Quo of Radiology Reporting
		Take-Home Message: Artificial Intelligence (AI) in Musculoskeletal Radiology
		References
	35 AIM and Explainable Methods in Medical Imaging and Diagnostics
		Introduction
		Medical Imaging in Clinical Diagnosis
		Recent Advances in Technology Toward Medical Imaging
		Machine Learning-Based Methods
		Explainable Artificial Intelligence: XAI
		Transparent Models
		Post Hoc Interpretability
		Explainable Models for Deep Neural Networks
		Conclusions and Future Landscape for ML-Based Imaging and Diagnostic Methods
		References
	36 Optimizing Radiologic Detection of COVID-19
		Introduction
		Test Set Technologies
		Artificial Intelligence in Medical Imaging and the Opportunity Provided by Test Sets
		Why Does Education Need to Embrace AI to Enhance Detection of COVID-19?
		AI-Tailored Education Using Clinician Demographics and Image Features
		AI Companions to Help with COVID-19 Diagnosis
		Finally, Some Cautionary Notes
		Conclusion
		References
	37 AIM in Surgical Pathology
		Introduction
		Definitions
		A Brief History
		Clinical Applications
			Detecting and Classifying Disease
			Grading and Scoring Disease
			Finding or Outlining Tumors and Tissue
			Finding Rare Events and Small Objects
			Predictive Tasks
			Image Quality Tools
		Grand Challenges
		Technical Aspects of Digital Pathology
		Standards of Reporting of AI
		Deployment and Regulation
			Technical
			Infrastructure
			People
		Education
		Conclusion
		Cross-References
		References
	38 Artificial Intelligence in Kidney Pathology
		Introduction
		Detection
			Detection Using Conventional Features
			Detection Using Deep Learning
		Segmentation
			Segmentation of Glomeruli
			Segmentation of Multiple Structures
		Classification
			Classification of Major Pathological Findings
			Classification and Identification of Specific Components
			Classification Based on Pathological Category
			Classification of Images with Immunohistochemistry
			Classification Based on the Clinical Category and Genotype
		Summary and Future Implications
		Equations
		References
	39 AIM in Dermatology
		Introduction
		AIM in Dermatology
			Area of Application: Skin Cancer
			Area of Application: Psoriasis
			Area of Application: Eczema
			Area of Application: Other Skin Disorders
			Dermatologist Attitude Toward Artificial Intelligence
			Limitation of Artificial Intelligence in Dermatology
			Better Applicability of AI Thanks to Teledermatology
			Ethnic Variations as a Challenge in the Development of Algorithms
		References
	40 Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury
		Introduction
		AKI Overview
		Background for AKI Prediction
		ML Model for AKI Onset Prediction
			Definition of AKI Event
			Prediction Timepoint and Target Period
			Input Features
			ML Algorithms
			Model Performance
			The External Validity of the Models
			Explainability of Models
			Implementation Challenges
		Conclusion
		References
	41 AIM in Hemodialysis
		Introduction
		Anemia, Hemoglobin Prediction, and ESA Dosage Optimization
			MPC-Based Approaches: Hemoglobin Prediction
				Physiologically Based Models
				AI-Based Models
					Linear Regression
					Feed-Forward Neural Networks
					Recurrent Neural Networks
					Other Supervised Learning Models
			Direct Dose Optimization
				Rule-Based Systems
				Reinforcement Learning
		Comorbidities, Mortality Prediction, and Patient Clustering
		Other Miscellaneous Applications
		Future Developments and Conclusions
		Cross-References
		References
	42 Artificial Intelligence in Public Health
		Introduction
		Defining Public Health and Social Medicine: Toward Greater Precision, or a Regression from the Collective to the Individual?
			Public Health Versus Individual Health, Social Medicine Versus Personalized Medicine
			Precision Versus Individualization. Precision Public Health
			Public Health, a Practice Based on Data, on Information, or on Evidence?
		Where Is Artificial Intelligence Used in Public Health?
			There Is No Artificial Intelligence Without Data: Data Federation and ``New´´ Types of Data
			The Citizen and the Patient: Producer, Actor, and Manager of Their Own Health
			AI and Health Surveillance Systems
			Learning Healthcare Systems (LHSs)
			Risk and Insurance
				The Notion of Risk in the Era of AI
				Approaches Adopted from Marketing and PR: Segmentation and Targeting
			Organization and Governance
				Capturing Data for Research and Governance
				New Public Health Actors: The Role of Platforms and the Private Sector
		Future Challenges
			Challenges Shared with Other Health Fields
			More Specific Challenges
			Conclusion and Outlook
		Cross-References
		References
	43 AIM and Business Models of Healthcare
		Introduction
		The Business Perspective: Product Development and Sales
		The Consumer Perspective: Purchaser, End User, and Patient
		Myth of Generalizability
			Proposed Consideration 1: Co-creation
			Proposed Consideration 2: Multi-Stakeholder Engagement
			Proposed Consideration 3: Metrics for Defining Value Delivered
		Ethics, Law, and Policy
		Conclusion
		References
	44 AIM for Healthcare in Africa
		Introduction
		Brief History of Artificial Intelligence for Medicine in Africa
			Current Applications of AI for Medicine in Africa
			Challenges with AIM Landscape in Africa
		Digital Health Foundation
		Data Availability and Quality
		Infrastructure
		Costs and Funding
		Governance, Regulations, and Ethics
			Critical Areas of Attention for Improving AIM in Africa
		Governance and Ethical Approaches
			Building for the Africa (How Best to Approach Solutions and Implementation)
			Opportunities for AIM Impact in Africa
			Overview of Selected Applications of AI for Healthcare in Africa
		Kenya Medical Supplies Agency (KEMSA) with IBM´s Watson
		Afya Pap in Zimbabwe
		Delft Institute´s CAD4TB Software
		References
	45 Aim in Climate Change and City Pollution
		Introduction
		Machine-Learning Methods in the Study of Urban Pollution
			ML Methods in Air-Pollutant Modeling
			ML Methods to Model Flow Dynamics
		Remote Sensing for Urban Air Observation
			Impact of Remote-Sensing Sensors for Monitoring Urban Airflow
			Remote-Sensing Data Resources and Analysis Methods
			Further Supportive Data that Satellite Remote Sensing Can Offer
		Challenges and Open Problems
		References
	46 AIM in Pharmacology and Drug Discovery
		Introduction
		Ligand Screening and Pharmacology
			Ligand-Based Approach
			Structure-Based Approach
			Chemical Genomics-Based Approach and Polypharmacology
		ADME in Pharmacokinetics
			Absorption
			Distribution
			Metabolism
			Excretion
		Data Source for Drug Discovery
		Omics Data
			Real-World Data
		Summary and Future Implications
		References
	47 Clinical Evaluation of AI in Medicine
		Clinical AI Systems Require Robust Clinical Evaluation
		Randomized Controlled Trials of Clinical AI Systems
			AI Systems for Disease Diagnosis
				AI Systems for Detection of Colonic Adenoma During Endoscopy
				An AI System for Detecting Blind Spots During Esophagogastroduodenoscopy
				An AI System for Detecting Paroxysmal Atrial Fibrillation
				An AI System for Diagnosing Childhood Cataracts
			AI Systems for Disease Prediction
				An AI Early Warning System for Detecting Intraoperative Hypotension
				An AI System for Mental Health Risk Assessment
			AI Systems for Adjusting Therapeutic Treatment
				An AI System for Optimizing Insulin Dose
				An AI System for Monitoring Drug Adherence
		Reporting Standards for AI Clinical Trials
			New Reporting Guidelines for AI Trials to Reflect the New Epoch
			Future Challenges
		References
	48 Artificial Intelligence in Medicine: Biochemical 3D Modeling and Drug Discovery
		Introduction
		Predicting the 3D Structures of Proteins
			Protein Folding
			Secondary Structure Prediction
			Ab Initio Tertiary Structure Prediction
				AlphaFold
		In Silico Drug Discovery
			Drug Repurposing
				Deep Neural Networks
				Generative Adversarial Networks
				Inputting Molecular Structures
		Conclusion
		References
	49 AIM in Endocrinology
		Introduction
		AI Applications in Diabetes Mellitus
			AI-Driven Diabetes Care: Changing the Landscape
			Prediction of Diabetes Risk
			Retinopathy Detection
			Prediction of Diabetic Complications
			Continuous Glucose Monitoring and Closed-Loop Artificial Pancreas System
			Therapeutic Lifestyle Modification
		AI Applications in Bone and Mineral Disorders
			Fracture Identification
			Opportunistic Screening of Osteoporosis and Sarcopenia
			Fracture Risk Assessment
			Finding Novel Biomarkers Related to Bone Metabolism
		AI Applications in Thyroid Disorders
			AI Application in Thyroid Cancer
			AI Application in Functional Thyroid Disorders
		AI Applications in Pituitary and Adrenal Disorders
			Diagnosis and Subtyping
			Prediction of Treatment Outcomes
		Implications of AI for the Endocrinologists
		References
	50 Artificial Intelligence and Hypertension Management
		Introduction
		Artificial Intelligence Approaches for Hypertension Management
			AI-Surrogate Measurement for BP
				Surrogate BP Measurement Using Wearable Sensors
				Surrogate BP Measurement Using Smartphone Cameras
				Challenges
			AI-Factor Analysis for BP Changes
				Causal Inference Using ML for Hypertension
				Explainable AI for Hypertension
				Challenges
			AI-Forecasting for Future BP
				BP Forecasting with Cross-Sectional Data
				BP Forecasting with Time-Series Data
				Challenges
		Conclusions
		References
	51 Aim and Diabetes
		Introduction
		Problems Faced by People with Diabetes
		The AI Approach
			Expert Systems Versus Decision Support Systems
			Prevention and Prognosis
			Diagnosis of Diabetes
			Diabetes Management
			Lifestyle Interventions/Behavioral Change
			Risk Stratification
			Complications and Comorbidities
		Discussion
		Conclusions
		References
	52 AIM in Primary Healthcare
		Introduction
		Opportunities of AI in Primary Care Include:
		Shift of Balance in Healthcare
		Electronic Health Records and Data Ownership
		Global Macrotrends [1-3]
		Symptom Checkers and Dissemination of Specialities
		Altered Roles
		Precision Medicine and Frontiers for AI
		Legal and Regulatory Aspects
		Privacy Concerns
		Patient Safety
		Medical Imaging Diagnostics and Radiology
			Medical Informatics and Clinical Decision Support
		Patient´s Perspective
		Gender Aspects
		Point-of-Care Dermatology and Ophthalmology
		Public Health Aspects on Primary Healthcare
		AI for General Practice Management
		Endocrinology and Diabetics
		Cardiovascular and Respiratory Management
			Cardiac
			Hypertension
			Respiratory
		Chronic Neurological and Neuropsychiatric Disease Monitoring
		Obstetrics, Pregnancy, and Pediatrics
		Oncology
		Conclusion
		Cross-References
		References
	53 AIM in Nursing Practice
		Chapter Introduction
		Introducing the IRs
		Brief History of AI
		Made not Born: The Beginning
		Definition
		Subsets of AI
		Supervised Machine Learning (SML)
		Unsupervised Machine Learning (UML)
		Reinforcement Machine Learning (RML)
		A Case for ICU
		Deep Machine Learning (DML)
		Nursing
		Nursing Practice
		Societal Change
		AI and Nursing Education
		Enhancing Holistic Care
		Robots to the Rescue
		The Future of Robotics
		Conclusion
		References
	54 AIM in Respiratory Disorders
		Introduction
		Imaging
			Chest X-ray
			Computed Tomography (CT)
			Histopathology
			Bronchoscopy
			Point-of-Care Ultrasound (PoCUS)
		Lung Function Testing
			Pulmonary Function Tests
			Spirometry
			Forced Oscillation Tests
		Telemedicine
		Miscellaneous
			Sleep Monitoring
			Breath Analysis
			Lung Sound Analysis
		Conclusions and Future Perspectives
		References
	55 AIM in Rheumatology
		Introduction
		Application of Artificial Intelligence in Rheumatology
			Application in Electronic Health Records
			Application in Genetic and Biomarker Data
			Application in Medical Images
			Application in Mixture Data
			Future Perspectives and Challenges
			Conclusions
		References
	56 AIM in Osteoporosis
		Introduction
		Methods
			Nonsparse Classification Techniques: Texture-Based, Patch-Based, and Deep Learning
			Classifiers and Discriminant Functions
			Bag of Keypoints
			Deep Neural Networks
			Sparse Representation and Classification
			Integrative Ensemble Sparse Analysis Techniques
		Results
		Discussion
		References
	57 Artificial Intelligence in Laboratory Medicine
		Introduction
		A Gentle Introduction: What Is Machine Learning?
		A Brief Overview of Machine Learning Implementation in Laboratory Medicine
		Machine Learning Models in Laboratory Medicine
		Conclusion
		References
	58 Artificial Intelligence in Medicine (AIM) in Cardiovascular Disorders
		Introduction
		Overview of AI Research on Cardiovascular Disorders
		Research in General Cardiovascular Disorders
		Research Targeting Ischemic Heart Diseases
			AI in Noninvasive Evaluations
			AI During Invasive Procedures
			AI in Risk Assessments
		Research Targeting Heart Failure
			AI in Diagnosing Heart Failure
			AI to Predict Prognosis of Heart Failure
		Research Targeting Arrhythmias
			AI to Diagnose Arrhythmias
			AI to Monitor for Arrhythmias
			AI to Identify Arrhythmias from Sources Other than ECGs
			Discussion
		References
	59 AIM in Medical Robotics
		Introduction
		Pre-operative Planning
		Pre-/Intra-operative Registration
		Execution
			Intra-operative Image Analysis
		Monitoring and Assessment
		Conclusion
		Cross-References
		References
	60 AI in Surgical Robotics
		Introduction
		Cognitive Surgical Robots
		Proprioception
			Depth Perception
			Navigation
		Surgical Tool Tracking
		Haptic Feedback and Tissue Interaction Sensing
		Advanced Visualization with Augmented Reality
		Robot-Assisted Task Execution
		Context-Aware Decision Support
		Outlook
		References
	61 Artificial Intelligence in Surgery
		Introduction
		AI-Powered Techniques
			Computer Vision
				Technical Aspects of Computer Vision in Surgery
				Computer Vision and Supervised Learning
				Computer Vision and Unsupervised Learning
			Natural Language Processing
		Current Applications of AI in Surgery
			Preoperative Risk Prediction
			Intraoperative Video Analysis
			Surgical Workflow Analysis
		Regulatory and Legal Considerations
		Conclusion
		References
	62 Artificial Intelligence in Urology
		Introduction
		Artificial Intelligence in Urology
			Urologic Oncology
				History
				Prostate Cancer
				Kidney Cancer
				Urothelial Cancer
			Endourology
			Andrology
				Prediction of Male Reproductive Potential
				Semen Analyses
				Predicting Sperm Retrieval Success Rates
				Predicting Surgical Shunt Intervention for Priapism Management
		Conclusion
		References
	63 Artificial Intelligence in Trauma and Orthopedics
		Introduction
		Diagnostics
			Musculoskeletal Image Scheduling and Protocoling
			Musculoskeletal Image Acquisition
			Musculoskeletal Image Interpretation
				Fracture Detection
				Knee Pathology Detection and Segmentation
				Osteoarthritis Detection and Cartilage Segmentation
				Orthopedic Implant Detection
				Orthopedic Oncology Detection
		Intraoperative and Robotics
			Semiautonomous Intraoperative Robotics
			Autonomous Intraoperative Robots
			Continued Adoption of Robotics
		Predictive Analytics
			Orthopedic Databases
			Predicting Disease Onset and Degree
			Postoperative Complications and Rehabilitation
			Conclusion
		References
	64 Harnessing Artificial Intelligence in Maxillofacial Surgery
		Introduction and Background
		The Maxillofacial Surgeon and AI
			Is AI a Friend or Foe?
			Simplifying AI for the Surgeon: Suggestions for Seamless Integration of AI and Surgery
		Machine Learning and Deep Learning
			Artificial Neural Networks
			Natural Language Processing
			Computer Vision
		Surgeon Dilemmas on AI
			Role of Surgeons in Enabling AI-Assisted Maxillofacial Surgeries
		Challenges in the Path
			Suggestions for Overcoming This Challenge
			Suggestions for Overcoming This Challenge
			Suggestions for Overcoming This Challenge
		Literature Speaks: An Overview of Current Applications with Potential for Harnessing AI in Maxillofacial Surgery
			Maxillofacial Presurgical Imaging
				Current
				Potential
			Orthognathic Surgery
				Current
				Potential
			Implant Surgery
				Current
				Potential
			Temporomandibular Joint (TMJ) Surgery
				Current
				Potential
			Oncosurgery and Reconstruction
				Current
				Potential
			Trauma Surgery
				Current
				Potential
			Impacted Teeth and Minor Oral Surgery
				Current
				Potential
			Miscellaneous
		Watch List for Future Forward Maxillofacial Surgeons
			Surgical Data Science
			Surgical Scene Analytics
			Surgical Control Tower (SCT)
		Conclusion
		References
	65 AIM in Dentistry
		Introduction
		AI for Assessment in Dentistry
		AI for Diagnosis in Dentistry
		AI for Treatment Planning
		AI for Outcome Prediction in Dentistry
		Concluding Remarks
		References
	66 Artificial Intelligence in Gastroenterology
		Introduction
		GI Endoscopy
		Existing Methods
			Hand-Crafted-Feature-Based Approaches
			Deep Learning-Based Approaches
			Unsupervised and Semi-supervised Approaches
		Example Results
		Open Issues and Ongoing Research
			Limited Data Availability
			Generalizability
			Metrics and Evaluation
			Automatic Report Generation
			Explainability
			Competitions and Challenges
			Clinical Verification and Emerging Commercial Systems
		Summary and Conclusions
		References
	67 AIM in Endoscopy Procedures
		Introduction
		Applications of Artificial Intelligence to Endoscopy Practice
			Detection and Diagnosis During Endoscopic Procedure
			Informative Frame Selection
			Mosaicking and Surface Reconstruction
			Augmented Reality Systems for Intraoperative Assistance and Surgeon Training
			Discussion and Perspectives
			Conclusion
		References
	68 AIM in Barrett´s Esophagus
		Introduction
			Problem Statement
			The Case for AI in the Esophagus
		AI for Barrett´s Esophagus
			BE Cancer Detection Using White Light Endoscopy
			BE Cancer Detection Using Narrow-Band Imaging
			BE Cancer Detection Using Endomicroscopy
			AI for Quality Assessment in the Esophagus
		Discussion
		References
	69 Artificial Intelligence for Colorectal Polyps in Colonoscopy
		Introduction
		Components for Developing DL
			Datasets
			Polyp Detection and Localization
			Polyp Segmentation
			Polyp Classification
			Conclusions and Future Trends
		Cross-References
		References
	70 AIM in Otolaryngology and Head and Neck Surgery
		Introduction
		A Brief Introduction into Machine Learning
			Artificial Intelligence in ENT
				Head and Neck Cancer
					Radiomics
					Radiological Staging
					Clinical Head and Neck Oncology
					Histopathology
					Multispectral Imaging
					Genetics and Molecular Markers
				Thyroid and Endocrine Surgery
					Thyroid Cancer
					Parathyroid Surgery
				Otology
					Imaging Modalities and Radiomics
					Auditory Brainstem Response Interpretation
					Sensorineural Hearing Loss Prediction
					Hearing Impairment Technologies
					Balance and Vestibular Pathologies
				Rhinology
					Imaging Diagnosis
					Pathological Diagnosis
					Chronic Rhinosinusitis Endotyping
					Laryngology
					Voice and Larynx
					Swallow
			Limitations, Challenges, and the Future
		Conclusion
		References
	71 AIM in Obstetrics and Gynecology
		Introduction
		AI in IVF
		Ethical Challenges
		References
	72 AIM in Medical Disorders in Pregnancy
		Introduction
		Artificial Intelligence in Reproductive Medicine
			Opportunities and Limitations of AI in Reproductive Medicine
			AI for Assessment, Diagnosis, or Treatment of Infertility
			AI for Embryo Annotation, Evaluation, and Selection
			AI for Prediction of Embryo Chromosome Status (Ploidy)
			AI in Maternal Healthcare Miscarriage Prediction
			Conclusion
		References
	73 AIM and Gender Aspects in Reproductive Medicine
		Introduction
		The History of Reproductive Medicine
		The Relationship Between AI and Reproductive Medicine
			Gender and Reproductive Medicine: Objectives, Issues, and Challenges
		Reproductive Medicine and Gender: A Century-First Century Approach
			The Male Experience and Reproductive Medicine
			Transgender, Gender Fluid, and Nonbinary Perspectives and Reproductive Medicine
		Concluding Thoughts
		References
	74 Artificial Intelligence in Pediatrics
		Introduction
		Challenges in Pediatric AI
		Recent Developments in Pediatric AI
			Cardiology
			Respiratory
			Genetics
			Endocrinology
			Neonatology
				Neonatal Sepsis
				Jaundice
			Ophthalmology
			Primary Care
			Radiology
			Pediatric Intensive Care
			Gastroenterology
		Future Potential for AI in Pediatrics
		References
	75 AIM in Neonatal and Pediatric Intensive Care
		Introduction
		Sepsis Definition
		Continuous Vital Sign Assessment to Predict Life-Threatening Events
		Neonatal Sepsis and the NICU
		Pediatric Sepsis and Early Detection
		Challenges and Future Perspectives of Automated Vital Signs Pattern Analysis
		Conclusions
		References
	76 Aging and Alzheimer´s Disease
		Introduction
		AI in Healthcare
			Historical Overview
				From the 1950s to the 2000s
				After the 2000s
			AI for Drug Discovery
				Virtual Screening
				Bioactivity Scoring
				ADME/T Properties Prediction
				De Novo Drug Design
			AI in Biology
				Genetics
				Proteomics
			AI in Medicine
				Diagnosis
				Prognosis
				Aging Biomarker Development
		Application of Machine Learning in Clinical Work for Alzheimer´s Disease
			Etiology
			Diagnosis
			Therapy
			Prognosis
		Future Perspectives and Concluding Remarks
		References
	77 Aim in Genomics
		Network Medicine: A New Paradigm for the Study of Diseases
			An Introduction to Network Medicine
			Machine Learning Challenges in Network Medicine
			A Network-Based Analysis of Disease Modules Using a Taxonomic Perspective
		Construction of the Interactome Taxonomy (I-T)
		Taxonomy Alignment and Labeling
			Taxonomy Alignment
			Comparing Alternative Induced Taxonomies
			Interactome Hierarchy (I-T) Labeling
		Experimental Set-up
		Discussion
			Finding Disease Categories with a Corresponding Dense Neighborhood in the Interactome
			Finding Unexpected Structural Relations Between Disease Categories
			Detection of Nomenclature Errors in Disease-Gene Associations
		Conclusions
		References
	78 AIM in Genomic Basis of Medicine: Applications
		Introduction
		Classification of Genomic Variation
			Variants in the Coding Region
			Variants in the Non-coding Region
		Interpretation of Variants Using NLP
		Diagnosis (Phenotyping)
		Proposal for Optimal Drug Treatment
		Summary and Future Implications
		References
	79 Stem Cell Progression for Transplantation
		Introduction
		Implementation of AI in Stem Cell Progression
			Machine Learning
			Computational Methods
			Deep Learning
		Conclusions
		References
	80 Artificial Intelligence in Blood Transcriptomics
		Introduction
		Blood Transcriptomics in Clinics: Methods, Features, Pitfalls
		Background on Artificial Intelligence in Biology
		Research Development Towards Clinical Applications
		Ethical Considerations, Data Security and Federated Learning
		Outlook
		Cross-References
		References
	81 AIM in Health Blogs
		Introduction
		Related Studies
			Social Analytics for Healthcare
			Adverse Drug Reaction (ADR)
		Methodology
			Data Gathering
			Data Filtering
			Topic Modeling
				Dimensionality Reduction
				Agglomerative Hierarchical Clustering
				Summarization
		Evaluation and Experiments
			Dataset and Diseases
			Compared Systems
			Evaluation Strategy
			Topic Analysis and Subclustering
		Data Analytics
		Conclusion
		References
	82 AIM and Transdermal Optical Imaging
		Introduction
			Characterizing the Cardiovascular System: Benefits, Obstacles, and Breakthroughs
				Blood Pressure Measurement
					Hypertension Screening, Diagnosis, and Management
					General Health Assessment
					General Health Monitoring
				Heart Rate, Heart Rhythm, and Heart Rate Variability
					General Assessment and Monitoring
					Stress Assessment
				The Need for Technological Breakthrough
		Transdermal Optical Imaging
			Overcoming Measurement Obstacles with Transdermal Optical Imaging Technology
			Scientific Foundations of Transdermal Optical Imaging
				Biomechanics and Video Capture
				Extracting Plethysmographic Signal from Video Using Artificial Intelligence
				Cardiovascular Parameters and Their Relation to Blood Flow Features
					Pulse Rate
					Pulse Rate Variability
					Blood Pressure
				Predicting Blood Pressure Through the Efficient Combination of Feature Information Using Artificial Intelligence
			Present Advances Using Transdermal Optical Imaging
				Accurate Blood Pressure Measurement
				Accurate Heart Rate and Heart Rate Variability Measurement
			Trends in Medicine and the Potential Impact of TOI
				Growing Challenges for Healthcare Delivery
				Improving Healthcare Quality and Efficiency with Patient-Centered Health Monitoring
				Improving Healthcare Accessibility with Telemedicine
				TOI-Based Tools Could Transform Personalized Self-Monitoring and Telemedicine
			Future Uses and Challenges for TOI
		References
	83 AI in Longevity Medicine
		Introduction
			The Advent of Deep Aging Clocks
			Federated Learning for Biomarker Discovery and Development
		Longevity Physicians: Emerging Specialists and the Need of a Tailored Education
		Healthy Versus Wealthy Longevity: Longevity Medicine and Public Health
		AI Applications in Medicine: The Fundament of Precision Medicine
		Conclusion and Future Perspectives
		References
	84 AIM in Nanomedicine
		Introduction
		Review of Literature
		Results and Discussion
		Machine Learning for Nanodrug Discovery and Nanoformulations
			Drug Delivery Systems and Formulation
		Machine Learning in Specific Areas of Nanomedicine
			Mathematical Machine Learning Modeling for Cancer Nanomedicine and Theranostics
		Machine Learning in Precision Nanotheranostics for Cancer
		Machine Learning for Nanotoxicology
		Machine Learning and Quantum Enabled Technologies
		Machine Learning and Regenerative Nanobiology
		Next Generation Machine Learning for Nanorobotic Surgery
		Ethics and Regulation
		References
	85 AIM in Wearable and Implantable Computing
		Introduction
		Context Awareness
			Definition and Theory
			Context Types and Recognition Path
		Selected Recognition Problems
			Context Pattern Spotting and Interpretation
			Interpretation from Context Hierarchy
			Flu Detection
			Situation Interpretation in Implants
		Design and Construction
			Topology Design and Optimization
			Wearable Personalization
			Prefabrication and Process Planning
			AI-Assisted Fabrication of Wearables
		Validation
			AI System safety assessment
			Implantable System Testing
			Self-Checking AI Systems
			Usability
		References
	86 Machine Learning and Electronic Noses for Medical Diagnostics
		A Brief Introduction to the Electronic Nose Technique
		An Overview of Electronic Nose Application in Medical Diagnostics
		Machine Learning in Enhancing the Diagnostic Capability of Electronic Noses
			Denoising Algorithms
			Sensor Drift Compensation
		Conclusions
		References
	87 Artificial Intelligence in Telemedicine
		Introduction
		The Basics of Telemedicine
		The Integration of Artificial Intelligence in Telemedicine
		Telemedicine and Artificial Intelligence
			Teleophthalmology and AI
			Telestroke and AI
			Teledermatology and AI
		Telemedicine, Artificial Intelligence, and Education
		References
	88 AIM and mHealth, Smartphones and Apps
		Introduction
			History
		AI and mHealth in Various Medical Specialties
			AI and mHealth for Evidence-Based Medicine
			AI and mHealth in the Field of Genomics
			AI and mHealth in the Field of Cardiovascular Medicine
			AI and mHealth in the Field of Respiratory Medicine
			AI for mHealth for Neuroscience and Neuropsychiatric Disorders
			AI in mHealth for Rheumatology
			AI in mHealth for Gastroenterology
			AI in mHealth for Urology
			AI in mHealth for Endocrinology
			AI in mHealth for Dermatology
			AI in mHealth for Obstetrics and Gynecology and Pediatrics
			AI in mHealth for Consensus Evaluation
			AI and mHealth in Infectious Diseases
			Summary and the Future of mHealth
		References
	89 AIM in Alternative Medicine
		Introduction
		Methodological Approaches
			Data Sources
			Knowledge Engineering Approaches
				Expert System
				Ontology and Knowledge Graph
			Machine Learning and Data Mining Approaches
			Applications and Related Work
				Clinical Diagnosis
				Clinical Therapies
				Pharmacological Applications
				Biomechanisms of Syndrome
		Shortcomings and Future Directions
		Conclusion
		References
	90 AIM in Oncology
		Introduction
		AI Tools
		Cancer Detection
		Cancer Treatment
		Conclusion
		References
	91 Artificial Intelligence in Radiotherapy and Patient Care
		Introduction
		Basic Concept of AI and Machine Learning
		Radiotherapy Chain
		Applications of AI and Machine Learning in Radiotherapy
			Radiation Treatment Planning
			Treatment Plan Evaluation
				Treatment Plan QA
				Dose Distribution Index Prediction
			Radiation Dose Delivery Using Multileaf Collimator
			Chatbot
				Background and Basic Concept of a Chatbot
				Chatbot for Radiotherapy Education and Patient Care
		Conclusion and Future Prospective
		References
	92 Deep Learning in Mammography Breast Cancer Detection
		Introduction
		Datasets
			MIAS
			DDSM
			INbreast
			OPTIMAM Mammography Image Database (OMI-DB)
			BCDR
		Deep Learning Methods
		Performance Metrics
		Discussion and Future Challenges
		Conclusion
		References
	93 AIM for Breast Thermography
		Introduction
		Conventional Breast Imaging Techniques
			X-Ray Mammography
			Ultrasound
			Magnetic Resonance Imaging
		Challenges with Conventional Breast Imaging Modalities
		Infrared Thermography for Breast Imaging
			Breast Thermal Imaging Protocol
		Challenges with Manual Interpretation of Breast Thermography
		Artificial Intelligence for Breast Thermography
			AI for View Labeling
			AI for Breast Segmentation
			AI for Malignancy Classification
			AI for Risk Estimation
			AI for Biomarkers Prediction
		Discussion
		Conclusion
		References
	94 AIM and Cervical Cancer
		Introduction
			Cervical Cancer and the Role of Pap Smear Test in Early Detection
			Pros and Cons of the Automated System of Diagnosis Concerning the Manual Diagnosis
			Challenges and Advances in Automation of Pap Smear Images
		Line of Action or AI-Driven Approaches for the Automation Task Using Pap-Smear Images
			Pap Smear Image Database Generation
		Ground Truth Labeling
		Automated Cell or Nuclei Segmentation from Whole Slide Image
		Feature Extraction and Feature Selection Methods
		Automated Binary or Multi-Class Classification of Pap Smear Images
		Summary
		References
	95 Artificial Intelligence in Infectious Diseases
		Introduction
		Applications of Artificial Intelligence in Infectious Diseases
		Artificial Intelligence for the Identification of Microorganisms
			Microorganisms Detection, Identification, and Quantification
			Evaluation of Antimicrobial Susceptibility
			Diagnosis, Disease Classification, and Clinical Outcomes
		Artificial Intelligence for the Clinical Diagnosis and Management of Infectious Diseases
			Early Detection and Management of Sepsis
			Diagnosis of Infection
			Prediction Tools
			Antimicrobial Selection
		Artificial Intelligence in Surveillance and Infection Prevention
		Challenges and Limitations in the Development and Application of Artificial Intelligence in Infectious Disease Management
			Development, Implementation, and Adoption
		Conclusion and Recommendations for Artificial Intelligence in Infectious Diseases
		References
	96 Artificial Intelligence in Epidemiology
		Introduction
		Present Advances
			Collecting Data for AI Development and Developping AI for Data Collection
				AI to Collect, Classify, and Structure Data
				AI to Reconstruct or Virtualize Experimental Designs
			Disease and Health Outcomes Surveillance
			Pharmacovigilance
			Sentiment Analysis and the Use of Social Media as Alternative Data Source and Data Processing Method in Epidemiology
				Recruitment of E-cohorts and Online Syndromic Surveillance
				Social Media Content and Sentiment Analysis
			From Data to Decision: Data- and Model-Driven Knowledge and Decision in Epidemiology
		Potential Trends and Future Challenges
			The Challenges Related to Data
			The Challenges Related to Epidemiology as an Explanatory Science: Statistical Inference and Causality
			The Challenges Related to the Use Made of Epidemiology by Decision-Makers and the Involvement of Private Actors
		Conclusion
		References
	97 Artificial Intelligence and Malaria
		Introduction
			Malaria Diagnosis: Parasite Detection and Species Identification
			Vector Ecology: Species, Biology, and Behaviors
			Deployment of Artificial Intelligence in Malaria
		Applications of Artificial Intelligence in Malaria Diagnosis and Malaria Vector Characterization
			Image-Based Automatic Classification for Malaria Diagnosis
				Plasmodium falciparum Detection
				Determination of Plasmodium Life Stages
				Mobile Applications and End-to-End Systems for Parasite Identification and Density Determination
			Image-Based Automatic Classification of Mosquito Vectors
				Mosquito Species Identification
				Mosquito Behavioral Patterns
			Characterization of Anopheles Biological Features Using Proteomic Tools
		Conclusion
		Cross-References
		References
	98 Artificial Intelligence in Infection Biology
		Introduction
			Aim in the Infection Biology
			Computer Vision in Infection Biology on Nano- and Microscale
			Computer Vision in Infection Biology on Mesoscale and Aspects of Temporal Dimensions
			Computer Vision in Infection Biology on Macroscale and Digital Biomarkers
			Artificial Intelligence in Molecular Infection Biology
			Conclusions
		References
	99 Artificial Intelligence in Medicine: Modeling the Dynamics of Infectious Diseases
		Epidemiology
			History
		Using Artificial Intelligence
			The Recurrent Neural Network Approach
			The Multi-agent Approach
				SARS-CoV-2
				Computing Requirements
			Parameter Uncertainty
		Conclusion
		References
	100 AI and Immunoinformatics
		Introduction
		AI and Vaccine Discovery
		AI and Vaccine Discovery for SARS-CoV-2
		AI for MHC Binding Peptide Prediction and Organ Transplant
		The Challenge of Variation
		Drug Discovery and Vaccine Discovery Differences for Learning
		Discussion
		References
	101 Artificial Intelligence in Clinical Immunology
		Introduction
		Healthcare Data as Big Data
		Structuring Healthcare Data
		What Is AI and How Does It Work?
		AI and the Learning Health System
		Applications of AI in Clinical Immunology
			Disease Diagnosis: Primary Immunodeficiency
		Clinical Decision Support
		Candidate Selection for Clinical Trials and Patient Identification Efforts
		COVID19 Applications and Impact
		Biomarker Identification in Clinical Immunology
		Microbiome Analysis
		Cytometric Analysis
		AI Governance for CI
			Ethical Implications in AI
		Conclusions
		References
	102 AIM in Allergy
		Introduction
		General Principles of Machine Learning
		The Allergic March: Can We Predict the Direction?
		The Needle in the Hay: ML-Approaches for Allergy Biomarker Discovery
		Finding a Pattern: Disease Subtyping for Clinical Management
		Bridging the Gap: Insights into Disease Biology by Integrative Analysis
		Challenges and Promising Research Directions in ML Applications
		References
	103 AIM in Haematology
		Introduction
		Discussion
			History
			Artificial Intelligence in Diagnostic Haemopathology
				Use of Neural Networks for Peripheral Blood Film Analysis
				Classification of White Blood Cells Using Transfer Learning
				Screening Blood Disorders like Thalassemia
				Screening for Polycythemia Rubra Vera
				Screening for Pyruvate Kinase Deficiency
				Digital Morphological Analysis
			Artificial Intelligence in Haem-oncology, Cancer Stem Cells, and Cancer Immunotherapy
				AI for Haem-oncology Screening
				AI for Immune Checkpoint Blockade Discovery
				Leukaemia and Lymphoproliferative Disorders
				AI for Acute Myeloid Leukaemia
				AI for Acute Promyelocytic Leukaemia
				AI for Lymphoma
				AI in Acute Lymphoblastic Leukaemia
				AI in Multiple Myeloma and Cancer Stem Cells
				AI in Neoplastic Bone Marrow Disease
				AI for Lymphoproliferative Disorders
				AI for Cancer Immunotherapy
				AI for Haematological Gene Profiling and Molecular Sequencing
				AI for Cancer Disease Progression Monitoring
			AI for Haemo-virology, Haemo-parasitology, and Immune System Studies
				AI in COVID-19 Haemo-virology
				AI in HIV and AIDS Haemo-virology
				AI in Haemo-parasitology
			AI for Haemorrhage Prediction and Transfusion
				AI for Transfusion and Blood Quality Haemo-diagnostics
				AI for Postpartum Haemorrhage Prediction
			AI for Bone Marrow Haematopoietic Stem Cell Transplantation
				AI in Autoimmunity
			Point-of-Care Diagnostics, Precision Diagnostics, and Sports Haematology
				AI in Point-of-Care and Precision Haematological Diagnostics
				AI in Sports Haematology
		Future Considerations for AI in Haematology
		References
	104 Artificial Intelligence in Medicine in Anemia
		Introduction
		Artificial Intelligence Tools for Anemia Management
			Pathophysiology and Treatment of Anemia in Chronic Kidney Disease
			Application of Artificial Intelligence to Diagnosis and Management of Anemia
			Expert Systems
			Moving Past Just Imitation
			Artificial Neural Networks
			Reinforcement Learning
			Fuzzy Systems
			Conclusion
		References
	105 AIM in Anesthesiology
		Introduction
		The Use of AI to Monitor Depth of Anesthesia
		Controlling Anesthesia Delivery with AI
		Perioperative Hemodynamic Optimization Assisted by AI
			Automation for Fluid Therapy
			Automation for Vasopressor Titration
			Automation for Inotrope Infusion
			Automation for Vasodilator Infusion
			Machine Learning for Predicting Hypotension
		Event and Risk Prediction with AI
		Other Applications of AI in Anesthesiology
		Technology Readiness Level of Published Applications
		Implications of AI for the Anesthesiologist
		Conclusion
		References
	106 Artificial Intelligence in Critical Care
		Introduction
		Interpretation, Explanation, Pipelines, and Guidelines
		Where at the ICU Should We Apply AI and ML?
		What Sort of AI and ML Can We Apply at the ICU?
		A Further Few Things About the Use of AI and ML in Medicine that Merit Discussion
		Conclusions
		References
	107 Artificial Intelligence in Medicine (AIM) for Cardiac Arrest
		Introduction
		Cause of Cardiac Arrest: Ventricular Arrhythmias
		Baseline Cardiovascular Diseases Leading to Ventricular Arrhythmia
		From Ventricular Arrhythmias to Cardiac Arrest
		The Current Clinical Treatment to Prevent Cardiac Arrest
		The Possibility and Advantage to Introduce AI Technology
		Overview of Researches to Prevent Cardiac Arrest Utilizing AI
		Early Recognition/Detection of High-Risk Patients
		Active Intervention and Follow-up
		Continuous Monitoring and Subsequent Interventions
		Discussion
		References
	108 Artificial Intelligence in Clinical Toxicology
		Introduction: Clinical Toxicology
			The Importance of Toxicovigilance
			Predicting Clinical Efficacy and Drug Toxicity
		Artificial Intelligence
			Machine Learning Algorithms
				Supervised Learning
				Unsupervised Learning
				Artificial Neural Networks (ANNs)
			Deep Learning Algorithms
		Advances in Computational Toxicology
			Big Data for Toxicology Interpretations
			Physiological-Based Pharmacokinetic (PBPK) Modeling
		Conclusion
		References
	109 Artificial Intelligence in Acute Ischemic Stroke
		Introduction
		AI Applications to Acute Stroke Medicine
			Diagnosis
				Lesion Segmentation
				Clot Detection
			Prediction
				Imaging Outcomes
				Clinical Outcomes
			Integration
		Challenges for AI in Stroke Medicine
			The Black Box Problem
			Evaluation of AI Models
			Data Registries
		Conclusion
		References
	110 Artificial Intelligence and Deep Learning in Ophthalmology
		Introduction
		Essential Concepts and Components in an AI System
		Application of AI and DL Algorithms in Ophthalmology Using Different Devices
			Retinal Fundus Photographs
				Diabetic Retinopathy
				Glaucoma
				Age-Related Macular Degeneration
				Retinopathy of Prematurity
				Papilledema and Optic Disc Abnormalities
				Systemic Diseases
			Optical Coherence Tomography
				DL Algorithms for Retinal Diseases Using Macula-Centered OCT
				DL Algorithms for Glaucoma Using Optic Disc-Centered OCT
				DL Algorithms for Glaucoma Using Anterior-Segment OCT
			Visual Fields
			Infantile Facial Video Recording
			Electronic Health Records
			Image Quality Assessment
		Future Research and Challenges
			Novel Technical Approaches
			Research Ethics and Artificial Images
			Data Ownership and Sharing
			Patients and Physicians Acceptance
			Education
			Guidelines
		Conclusion
		References
	111 Artificial Intelligence in Ophthalmology
		Introduction
		Clinical Application of AI in Ophthalmology
			Diabetic Retinopathy (DR)
			Glaucoma
			Age-Related Macular Degeneration (AMD)
		Conclusions
		References
	112 Aim in Depression and Anxiety
		Introduction
		AI and Machine Learning for Precision Medicine in Depression and Anxiety Disorders
			Aims for Using AI in Depression and Anxiety
			Risk
			Diagnosis
			Treatment Outcome
			Suicidality
			Relapse
			Outlook
		Cross-References
		References
	113 Artificial Intelligence for Autism Spectrum Disorders
		Introduction
		AI Applications for ASD: Objectives, Data, and Challenges
			Objective-Based Categorization
				Automatic/Early Diagnosis
				Severity Recognition
				Subtypes Definition
				Longitudinal Studies
				Explorative Analysis
				Drug Discovery
				Teaching and Interaction
			Field-Based Categorization
				Brain Structural Imaging
				Brain Functional Imaging
				Genetics
				Video and Sensor Analysis
				Miscellaneous
			Challenges of the AI-Based Research on ASD
		Conclusions
		Cross-References
		References
	114 Artificial Intelligence in Schizophrenia
		Introduction
		Artificial Intelligence Applied to the Research, Diagnosis, and Treatment of Schizophrenia and Related Disorders: Pre-2000
		Artificial Intelligence Applied to the Research, Diagnosis, and Treatment of Schizophrenia and Related Disorders: 2000-2012
		Artificial Intelligence Applied to the Research, Diagnosis, and Treatment of Schizophrenia and Related Disorders: 2012-2018
		Artificial Intelligence Applied to the Research, Diagnosis, and Treatment of Schizophrenia and Related Disorders: 2019-Present
		Current and Future Clinical Use of AI Techniques in the Diagnosis and Treatment of Schizophrenia and Related Disorders
		Cross-References
		References
	115 The Rise of the Mental Health Chatbot
		Introduction
		Mental Health Support using Chatbots
			Chapter Summary
			Mental Health Chatbots
			The Value of Mental Health Chatbots
			How Chatbots Work
		Economic Impact
			How a Chatbot Could Change the Economics of the Employee Benefits Industry
				Factors Causing Low Utilization
				Utilization Strategies of Change
				Factors Causing Low PEPM
				PEPM Strategies of Change
		Real-World Applications
			Case Study: 24/7 Access for 430,000 People Served by a Public Health Department
			Case Study: AI Mental Health Support for Young Mothers in Africa
			Case Study: Supporting Caregivers and Their Patients
			Case Study: Addressing Comorbidities in Childhood Obesity, Prediabetes, and Mental Health Struggles
		References
	116 AIM in Alcohol and Drug Dependence
		Introduction
		Challenges in Diagnosis and Treatment
		Role of Artificial Intelligence
			Data Sets for Machine Learning
			Machine Learning for Drug Dependence
			Challenges and Outlook
		Conclusion
		Cross-References
		References
	117 Artificial Intelligence in Medicine and PTSD
		PTSD: A Complex Clinical Disorder
			PTSD: A Psychological Disorder that May Appear After Exposure to a Traumatic Event
			PTSD: A Disorder that Is Currently Difficult to Explain and Predict
			The Evolution of PTSD Over Time May Be Complex and Chronic, and Associated with Physical and Mental Disorders of a Different N...
			Improving Screening for and Diagnosis of PTSD
		Present Advances: AI and PTSD
			AI and PTSD Prediction. AI for Clinical Practice and Practitioners
				Early Prediction of PTSD
				Prediction of Response to Treatment
			AI, Characterization and Diagnosis of PTSD. AI for Basic Research
				The Use of Genomic and Neuroimaging Data by AI
					Genomic Data
					Neuro-Imaging Data
				AI for the Diagnosis and Differentiation of PTSD from Other Mental Illnesses
				The Use of AI to Characterize Subtypes or Subsyndromic Forms of PTSD
				The Contribution of AI in Linking Basic Research to Clinical Applications. Neuroimaging Data
		Potential Trends and Future Challenges
		References
	118 AIM in Eating Disorders
		Introduction
			AI for ED
			Monitoring of Dietary Behavior
		Methods in Automated Dietary Monitoring (ADM)
			Wearable-Based ADM
			Smartphone-Based ADM
			Ambient Technology-Based ADM
		Digital Biomarkers for AIM in EDs
			Intake Timing
			Food Type
				Image-Based Food Type Recognition
				Audio-Based Food Type Recognition
				Evaluation Metrics for Food Type Recognition
			Food Amount
		Intake-Accompanying Phenomena Related to EDs
			Triggers and Stressors
			Example: AI in Anorexia Nervosa (AN)
				Prospects for AN
		References
	119 AIM in Neurology
		Introduction
		Childhood Medulloblastoma
		Methods and Materials for AI Application
		Significance of an Accurate Detection in Prognosis
		Challenges of a Clinical Observation
		Advantages of AI-Driven Solution
		Challenges of an AI-Driven Solution
		Scope for Industry Transformation
		Summary
		References
	120 AIM in Neurodegenerative Diseases: Parkinson and Alzheimer
		Introduction
		Artificial Intelligence for Dementia
			AI for Dementia Diagnosis Using Big Data
			Conditional Restricted Boltzmann Machines in Alzheimer´s Disease
			Computer Vision for Dementia Patient Video Monitoring and Analysis
			AI and Assistive Robotic Technologies for Dementia
			Cognitive and Behavioral Biomarker, Facial Motion Assessment Using AI
			Dementia-Related Electroencephalographic Analysis and Robotic-Assisted AI
			Vascular Dementia and AI
			Convolutional Neural Nets and Model Explainability in Dementia AI Studies
		Artificial Intelligence in Parkinson´s Disease
			AI in Lewy Body Dementia
			Motor and Gait Impairment Detection Using AI
			AI for Electroencephalographic Diagnosis and Prognostication in Parkinson´s Disease
			AI for Parkinson´s Disease Medical Management Drug Repurposing
			AI for Parkinson´s Disease Surgical Management
			Ethical and Social Implications of AI for Parkinson´s and Dementia
			Future In Vivo Detection and Management of Dementia and Parkinson´s Using Quantum AI Systems
		References
	121 AIM in Amyotrophic Lateral Sclerosis
		Introduction
		Review
			Clinical Trial Analysis of ALS Disease
			PRO-ACT Dataset
		Study of ALS with Machine Learning Approach
		Experimental Results
		Conclusion
		References
	122 AIM in Ménière´s Disease
		Introduction
		Overview of Ménière´s Disease
		The History of Inner Ear MRI to Visualize Endolymphatic Space and Hydrops
		Current Diagnostic Method and Dilemma in Ménière´s Disease
		Sequence and Analysis of Inner Ear MRI for the Diagnosis of Ménière´s Disease
		Development of Artificial Intelligence in the Medical Field: Focusing on Medical Image Analysis
		The Use of Artificial Intelligence in Ménière´s Disease
		The Future of Artificial Intelligence in Ménière´s Disease
		References
	123 AIM and Brain Tumors
		Introduction
			Magnetic Resonance Imaging of Brain Tumors
			Automated Analysis of MRI in Brain Tumors: Challenges
			Structure of the Chapter
		Brain Tumor Detection and Segmentation
			Automatic Detection and Segmentation of Brain Tumors
			Dealing with Limited Ground-Truth Data Sets
			Assessing Automatic Detection and Segmentation
		Analysis of Segmented Brain Tumors
			Quantification of Tumor Characteristics
			Classification of Brain Tumors Using AI
		Conclusion
		References
	124 Artificial Intelligence in Stroke
		Introduction
		Artificial Intelligence
		Early Detection of Stroke Symptoms
		Acute Stroke Therapy
		Role of AI in the Subacute Phase and Follow-Ups of the Ischemic Stroke Patients
		Use of AI in the Management of Transient Ischemic Attack
		Role of AI in the Management of Intracerebral Hemorrhage
		Future Directions
		References
	125 AIM in Clinical Neurophysiology and Electroencephalography (EEG)
		Introduction
		Epilepsy
		Artificial Intelligence, Machine Learning, and Deep Learning
		Machine Learning and Deep Learning Approaches in Epilepsy
			The Role of History-Taking and Where Deep Learning Can Make In-Roads
		Deep Learning Approaches for Investigating Epilepsy
			Machine Learning for EEG Analysis
		Deep Learning in Epilepsy Treatment
		Machine Learning for Epilepsy Surgery
		Machine Learning for Electrophysiological Migraine Detection
		Deep Learning for Electromyography
		Practical Machine Learning for EEG Analysis
		Concluding Remarks
			Highlighting the Tension Between Progress and ``Model Explainability´´
		References
	126 Artificial Intelligence in Forensic Medicine
		Introduction
		Evidence and Individualization in Forensic Medicine
			Data, Information, and Evidence in Medicine and in Law: AI at Every Step in the Process
			Personalized Medicine, Individualized Sentences: A Shift from the Group to the Individual in Society
			Forensic Reasoning
		Artificial Intelligence and Assisted Decision-Making in Forensic Medicine
			AI and Clinical Forensic Medicine
			AI and Forensic Pathology
			AI and Medical Expertise in the Legal Context
			AI on the Borders of Forensic Medicine
		Artificial Intelligence for Forensic Medicine Research
		Potential Trends and Future Challenges
		Cross-References
		References
	127 AI in Forensic Medicine for the Practicing Doctor
		Introduction
		AI Applications for the Forensic Medicine Practice
			Part A: Description of AI Applications for the Forensic Medical Doctor
				Thanatology
					Postmortem Identification
					Postmortem Interval (PMI) Estimation
					Determination of the Causes of Death
				Clinical Forensic Medicine
			Part B: Applicability and Usefulness of AI in Current and Future Practices in Forensic Medicine
		Conclusion
		References
	128 Artificial Intelligence for Physiotherapy and Rehabilitation
		Introduction
		AI in Physiotherapy and Physical Rehabilitation of Patients
		AI in Exergames and Serious Games for Early- Stage Physical Rehabilitation
		AI in Physiotherapy Education and Use of Simulation for Educating Physiotherapists
		AI and Physiotherapy Education
		AI for Robotic Assisted Physiotherapy
		AI for Physio-assisted Activity of Daily Living Monitoring
		AI and Virtual Reality for Physiotherapy and Rehabilitation
		AI for Physiotherapy-assisted Sensory and Balance Training
		AI for Assisted Wheelchair Users and Assisted Mobility Support
		AI for Inattention and Hemi-neglect Training
		AI for Respiratory Physiotherapy Management
		AI for Community Physiotherapy and Care
		AI for Cognitive Impaired Patients Needing Physiotherapy and Rehabilitation
		AI for Functional and Feedback Systems in Physiotherapy
		AI in Smart Watches and Wearables for Physiotherapy
		Future of AI in Physiotherapy
		References
	129 AIM in Rehabilitation
		Introduction
		Definition of Rehabilitation
			Benefits of Rehabilitation
			Phases of Rehabilitation
		Areas of Rehabilitation Medicine
			Physical Therapy
			Speech Therapy
			Neuropsychology
			Occupational Rehabilitation
		General Applications of AI in Rehabilitation
			Robotics in Rehabilitation
				Learning from Demonstration
					Gait Rehabilitation
			AI in Assessment and Decision Support Systems
			AI in Rehabilitation Prognosis
			AI Wearable Monitoring Devices
				Fall Detection
				Monitoring Purposes
			AI in Virtual Reality and Serious Games
		Summary
		References
	130 AIM in Sports Medicine
		Introduction
		Advances
		Potential Trends
		Future Challenges
		References
Index