Intelligent Systems in Medicine and Health: The Role of AI

Foreword
Preface
	The State of AI in Medicine
	Introducing Intelligent Systems in Medicine and Health: The Role of AI
	Structure and Content
	Guide to Use of This Book
Acknowledgments
Contents
Contributors
Part I: Introduction
	Chapter 1: Introducing AI in Medicine
		The Rise of AIM
			Knowledge-Based Systems
			Neural Networks and Deep Learning
			Machine Learning and Medical Practice
			The Scope of AIM
		From Accurate Predictions to Clinically Useful AIM
		The Cognitive Informatics Perspective
			Why CI?
			The Complementarity of Human and Machine Intelligence
			Mediating Safe and Effective Human Use of AI-Based Tools
		Concluding Remarks
		References
	Chapter 2: AI in Medicine: Some Pertinent History
		Introduction
		Artificial Intelligence: The Early Years
		Modern History of AI
		AI Meets Medicine and Biology: The 1960s and 1970s
			Emergence of AIM Research at Stanford University
			Three Influential AIM Research Projects from the 1970s
				INTERNIST-1/QMR
				CASNET
				MYCIN
			Cognitive Science and AIM
			Reflecting on the 1970s
		Evolution of AIM During the 1980s and 1990s
			AI Spring and Summer Give Way to AI Winter
			AIM Deals with the Tumult of the 80s and 90s
		The Last 20 Years: Both AI and AIM Come of Age
		References
	Chapter 3: Data and Computation: A Contemporary Landscape
		Understanding the World Through Data and Computation
			Types of Data Relevant to Biomedicine
			Knowing Through Computation
		Motivational Example
		Computational Landscape
			Knowledge Representation
			Machine Learning
			Data Integration to Better Understand Medicine: Multimodal, Multi-Scale Models
			Distributed/Networked Computing
				Data Federation Models
				Interoperability
			Computational Aspects of Privacy
		Trends and Future Challenges
			Ground Truth
			Open Science and Mechanisms for Open data
			Data as a Public Good
		References
Part II: Approaches
	Chapter 4: Knowledge-Based Systems in Medicine
		What Is a Knowledge-Based System?
		How Is Knowledge Represented in a Computer?
			Rules: Inference Steps
			Patterns: Matching
			Probabilistic Models
				Naive Bayes
					Bayesian Networks
					Decision Analysis and Influence Diagrams
			Causal Mechanisms: How Things Work
		How Is Knowledge Acquired?
			Ontologies and Their Tools
		Knowledge in the Era of Machine Learning
			Incorporating Knowledge into Machine Learning Models
			Graph-Based Models
				Graph Representation Learning
				Biomedical Applications of Graph Machine Learning
			Text-Based Models
			Leveraging Expert Systems to Train Models
		Looking Forward
		References
	Chapter 5: Clinical Cognition and AI: From Emulation to Symbiosis
		Augmenting Human Expertise: Motivating Examples
		Cognitive Science and Clinical Cognition
			Symbolic Representations of Clinical Information
			Clinical Text Understanding
		Clinical Cognition, Reasoning and the Evolution of AI
			Bridging Cognition to Medical Reasoning
			Models of Medical Reasoning
		Knowledge Organization, Expert Perception and Memory
		Understanding Clinical Practice for AI Systems
			The Role of Distributed Cognition
		AI, Machine Learning, and Human Cognition
		Reinforcing the Human Component
			Augmenting Clinical Comprehension
			Supporting Specific Cognitive Tasks
			Mental Models of AI Systems
		Conclusion
		References
	Chapter 6: Machine Learning Systems
		Identifying Problems Suited to Machine Learning
		The Machine Learning Workflow: Components of a Machine Learning Solution
		Evaluating Machine Learning Models: Validation Metrics
		Supervised Machine Learning
			The Structure of a Supervised Machine Learning Algorithm
			Supervised Learning: A Mathematical Formulation
			Augmenting Feature Representations: Basis Function Expansion
			Bias and Variance
			Regularization: Ridge and Lasso Regression
		Linear Models for Classification
			Discriminative Models: Logistic Regression
			Regularized Logistic Regression: Ridge and Lasso Models
			A Simple Clinical Example of Logistic Regression
			A Multivariate Clinical Example of Logistic Regression
			Generative Models: Gaussian Discriminant Analysis
			Factored Generative Models: Naive Bayes
			Bias and Variance in Generative Models
			Recap of Parametric Linear Models for Classification
		Non-linear Models
			Kernel Methods
			Similarity Functions for Kernel Methods
			Recap: How to Use Kernels for Classification
			Sparse Kernel Machines and Maximum Margin Classifiers
			Neural Networks: Stacked Logistic Models
			Parameterizing Feedforward Networks and the Forward Propagation Algorithm
			Learning the Parameters of a Feedforward Network
			Convolutional Networks
			Other Network Architectures
			Putting It All Together: The Workflow for Training Deep Neural Networks
		Ensembling Models
		Conclusion
		References
	Chapter 7: Natural Language Processing
		Introduction to NLP and Basic Linguistics Information
		Common Biomedical NLP Tasks and Methods
			Overview of Biomedical NLP Tasks
			Biomedical IE Tasks and Methods
				NER Examples and Methods
				RE Examples and Methods
				CN Examples and Methods
		Current Biomedical NLP Tools and Corpora
			Biomedical NLP Tools
			Biomedical Text Resources
				Types of Biomedical Text
				Annotated Corpora from Past Challenges
		Applications, Challenges and Future Directions
			Applications of NLP
			Challenges and Future Directions
			Conclusion
		References
	Chapter 8: Explainability in Medical AI
		Introduction
			Current Trends in AI Explainability Research
			Applying Additional Context to Understand Explainability in Medical AI
		Three Purposes of AI Explainability
		Expanding the Conception of AI Explainability Based on Cognitive Informatics
			Human Information Processing
			Human-AI Agents
			Sociotechnical Systems
		Implications of Explainability on Bias and the Regulatory Environment
			Explainability and Inherent Biases
			Effect of Explainability on Accountability for Decision Making
			The Current Regulatory Framework and Explainability
		Application of Explainability to Real World Examples of Medical AI
			Example: Continuous Blood Glucose Monitoring for Patients with Type 1 Diabetes
			Example: Digital Image Analysis Tools Assisting in Histopathological Diagnoses
			Example: Wearable Devices Informing Clinical Management
		Conclusion
		References
	Chapter 9: Intelligent Agents and Dialog Systems
		Introduction to Dialog Systems
			Definitions and Scope
			What’s Hard About Getting Machines to Engage in Spontaneous Human Conversation?
			Machine Learning and Dialog Systems
		History of Dialog Systems in Healthcare
		Dialog System Technology
			Classic Symbolic Pipeline Architectures
			Neural Network Methods and End-to-End Architectures
		Approaches to Dialog System Evaluation
			Evaluation of Pipeline Architectures
			Automated Metrics for End-to-End Architectures
			System-Level Evaluation
		Example Patient- and Consumer-Facing Dialog Systems
		Example Provider-Facing Dialog Systems
		Safety Issues in Dialog Systems for Healthcare
		State of the Art: What We Currently Can and Can’t Do
		Future Directions
		Conclusion
		References
Part III: Applications
	Chapter 10: Integration of AI for Clinical Decision Support
		Challenges Faced by Clinicians
		Artificial Intelligence-Based CDS
		Degree of Automation in AI-CDS
		Application of AI-CDS in Clinical Care
		Pitfalls of AI-CDS
		Regulation of AI-CDS
		Conclusions
		References
	Chapter 11: Predicting Medical Outcomes
		Clinical Outcomes: An Enlarged Perspective
		AI Approaches for Clinical Outcomes Prediction
			Preprocessing: Missing Values, Features Transformation and Latent Variables Extraction
				Missing Values
				Dimensionality Reduction and Feature Transformation
				Deep Learning
			Classification
			Regression
			Survival Analysis
			Time Lines and Trajectory Modeling
			Markov Models
		Performance Assessment
			Experimental Design for Learning
			Common Mistakes in the Design of Experimental Validation
			Experimental Design for Testing: External Validation
			Checking Performance Stability, Model Drifts, Diagnostics, and Model Revision
		Case Studies and Examples
			Type 2 Diabetes
			Myelodysplastic Syndromes
			The COVID-19 Pandemic
		Conclusion
		References
	Chapter 12: Interpreting Medical Images
		Overview
			Introduction to Medical Images
			Characteristics of Medical Images
		Historical Perspectives
			Pioneer CAD Systems
			Recent Successes in Deep Learning
		Clinical Needs and Existing Challenges
			Clinical Needs
			Medical Applications
			Technical Barriers
		Opportunities and Emerging Techniques
			Acquiring Annotation from Human Experts
			Utilizing Annotation by Advanced Models
			Extracting Features from Unannotated Images
		Conclusion
		References
	Chapter 13: Public Health Applications
		Public Health and AI
			Public Health, Essential Public Health Functions, and Public Health Informatics
			The Nature of Essential Public Health Functions and the Application of AI
			A Vision for AI in Public Health
		Applications of AI in Public Health
			Examples of AI Applications to Public Health Functions
				Assessment
				Policy Development
				Assurance
			Barriers and Risks to AI Applications in Public Health
		Future Applications of AI in Public Health
			Progress Towards the Vision
			Future Applications
		References
	Chapter 14: AI in Translational Bioinformatics and Precision Medicine
		Introduction and Concepts
			A Brief History of Translational Bioinformatics
			Concepts of AI in Translational Bioinformatics
			Primary Data Categories in Translational Bioinformatics
				Genomic Data
				Clinomic Data
				Phenotypic Data
			Categorizing AI Applications in Translational Bioinformatics
				G2G (Genomic to Genomic)
				G2P (Genomic to Phenotypic): Genome-Wide Association Studies (GWAS)
				P2P (Phenotypic to Phenotypic): Identify Disease Genomic Subtypes
				P2C (Phenotypic to Clinomic)
				C2C (Clinomic to Clinomic)
		Informatics Challenges in Translational Bioinformatics
			Big Data Characteristics
				Volume of Data
				Veracity of Data
				Variability of Data
				Velocity of Data
			Social-Economic Bias
			Domain Knowledge Representation and Interpretability
			Model Robustness and Quality Control
		Translational Bioinformatics Tools & Infrastructure
			Extended Data Management Systems
			Data Preprocessing Pipelines
				Pipelines to Build the Data Matrix
				Enhancing the Data Matrix
			Supervised and Unsupervised Learning
			Popular Algorithms in Translational Bioinformatics
				Classification Algorithms
				Clustering Algorithms
				Dimension Reduction Algorithms
				Association Mining Algorithms
			Security, Privacy, and Ethical Considerations (see also Chap. 18)
			Team Data Science Infrastructure
		Applications of AI in Translational Bioinformatics
			Improving Translational Bioinformatics Data Infrastructure
			Inferring Pairwise Molecular Regulation
			Inferring and Characterizing Cellular Signaling Mechanism that Determines the Cellular Response
			Identifying and Characterizing New Cell Types and Subtypes
			Drug Repurposing
			Supporting Clinical Decisions with Bioinformatics Analysis
			Predicting Complex Biochemical Structures
		Trends and Outlook
		References
	Chapter 15: Health Systems Management
		Promise of AI in Health Systems
		Example: Outpatient Scheduling
		Example: Device Monitoring
		Governance
		Concluding Remarks
		References
	Chapter 16: Intelligent Systems in Learning and Education
		Introduction
		Historical Evolution of Medical Education: Philosophical Perspectives and Related Educational Strategies
			Acquisition of Clinical Competence
			Cognitive Approaches to Learning and Instruction
		Approaches to Artificial Intelligence in Education and Training
			Artificial Intelligence Systems and the Individual Learner
			Computable Representations
			Intelligent Tutoring Systems
			Dialog Systems and Natural Language Processing
			Question Generation
			Dynamic Assessment, Feedback, and Guidance
			Machine Learning and Neural Networks
			Affect and Emotion Aware ITS
			Virtual and Augmented Reality
			Simulations and Serious Games
		Artificial Intelligence Systems and the Education Enterprise
			Learning Analytics
			Ethics and Regulation
			Technology Acceptance and Implementation
			Artificial Intelligence Systems in the Future Workplace
		The Road Ahead: Opportunities and Challenges for Intelligent Systems in Training, Learning and Practice
		References
Part IV: The Future of AI in Medicine: Prospects and Challenges
	Chapter 17: Framework for the Evaluation of Clinical AI Systems
		The Role of Evaluation: Why It Is Important
		Framing Questions for Assessing an Evaluation Plan
		Design and Iteration
		Cognitive Evaluation Methods
		Delivery of Decision Support
			Medical Device Data-Interpretation
			Event Monitoring and Alerts
			Direct Consultation with Clinical User
		Naturalistic Studies
			Is the System Accepted by Users?
			Does the System Have a Positive Impact on User Behavior?
			Do Patients Benefit When the System Is Used?
			Is Any Positive Outcome Worth the Associated Expense?
			Do All Patients Benefit? What Is the Impact on the Population as a Whole?
		Additional Considerations
		References
	Chapter 18: Ethical and Policy Issues
		Introduction to the Utility of Applied Ethics
		Software Engineering Principles, Standards and Best Practices
			Software Engineering of Dependable Systems
			Ethics in Good Engineering Practice
		Why Context Matters
			Trust and Trustworthiness
			Explainability and Interpretability
			Transparency
			The Need for Human Control
			Taking the Long View
		Fairness and Sources of Bias
			Fairness, Bias, Equality, Equity
			Data Sets
			Algorithmic Design
			Implementation and Algorithmovigilance
			Organizational and Economic Dimensions
			Recommendations for Identifying Bias
		Governance and Oversight
		AI at Large
			AI, Humanity, and Society
			AI and the Healthcare Professions
		References
	Chapter 19: Anticipating the Future of Artificial Intelligence in Medicine and Health Care: A Clinical Data Science Perspective
		Introduction
		AI in Medicine Technology: An Exponential Rise
			Current State
			Near Future State
			Future State
		The AI in Medicine Stakeholders: Increasing Gap to Technology
			Current State
			Near Future State
			Future State
		The AI in Medicine Dyads: Synergy and Beyond
			The Human-Human Dyad
			The Machine-Machine Dyad
			The Human-Machine Dyad
		Conclusion: Convolution to Consilience
		References
	Chapter 20: Reflections and Projections
		Introduction
		Explainability and Complementarity
		Restoring Knowledge to AIM
		Forward-Thinking Clinical Applications
		Workflow, and the Workforce
		Evaluation
		Concluding Remarks
		References
Terms and Definitions
Index