Appendix A — Quick Reference: All Chapter Summaries (TL;DRs)

About This Reference Guide

This appendix compiles all 21 chapter TL;DR summaries into a single reference document. Use this for:

Quick review before meetings or presentations
Rapid lookup when troubleshooting specific issues
Teaching materials for training sessions
Offline reference when you need key concepts without full chapter context

Reading time: ~45-60 minutes for all summaries (vs. 20+ hours for full handbook)

A.1 Part I: Foundations

A.1.1 Chapter 1: AI in Context: A Brief History

💡 The Big Picture: AI has experienced 70 years of hype cycles—from 1970s expert systems to today’s foundation models—with each wave promising revolution but delivering narrow applications. Understanding this history is essential for separating genuine breakthroughs from recurring patterns of overpromise.

⚠️ The Cautionary Tales:

MYCIN (1970s): Matched infectious disease experts in diagnosing blood infections (65% acceptability, 90.9% accuracy) but was never used clinically—killed by liability concerns, FDA uncertainty, integration challenges, and trust barriers
Google Flu Trends (2008-2015): Published in Nature, then overestimated flu activity by 140% due to overfitting to spurious correlations, algorithm changes, and lack of epidemiologist input—quietly discontinued
IBM Watson for Oncology: Jeopardy! champion failed in cancer treatment, producing unsafe and incorrect recommendations when deployed in real clinical settings

✅ What Actually Works:

Specific, well-defined problems (diabetic retinopathy screening, not “diagnose everything”)
Augments human expertise (radiology decision support, not replacement)
Integrates into workflows (fits existing processes vs. demanding wholesale change)
High-quality labeled data with measurable outcomes
Domain expert involvement from the start (not just engineers)

💡 Critical Insight: Technical excellence ≠ clinical adoption. The hardest problems deploying medical AI are legal, regulatory, social, and organizational—not algorithmic.

🎯 Takeaway: Be skeptical of grand claims. Demand rigorous real-world validation. Focus on deployment challenges from day one. Center domain expertise in AI development.

A.1.2 Chapter 2: Just Enough AI to Be Dangerous

💡 The Big Picture: You don’t need a PhD in machine learning—you need enough knowledge to choose the right tool, avoid catastrophic mistakes, and communicate with technical teams.

✅ Algorithm Selection Guide:

Logistic Regression: Your default starting point. Interpretable, works with modest data, epidemiologists understand it
Random Forests: Best first “real” ML model. Robust, handles messy data, works out-of-the-box
Gradient Boosting (XGBoost): Maximum accuracy for tabular data
Deep Learning: Only for images, text, or truly complex patterns

💡 Critical Reality Check: For most public health spreadsheet data (rows and columns), gradient boosting beats neural networks. Save deep learning for medical imaging and clinical notes.

❌ The Cardinal Sins:

Overfitting: Model memorizes noise instead of learning patterns
Data Leakage: Test information contaminates training
Ignoring Class Imbalance: Rare events need special handling
Temporal Violations: Never use future to predict past

💡 Most Important Lesson: Feature engineering (domain expertise) > algorithm sophistication. Epidemiologists have an advantage over pure data scientists—use it.

🎯 Takeaway: Start simple (logistic regression), add complexity only if needed. Trust your domain expertise for feature engineering. Most problems don’t need deep learning.

→ See also: Chapter 8 (Evaluation), Chapter 3 (Data Quality)

A.1.3 Chapter 3: The Data Problem

💡 The Big Picture: “Garbage in, garbage out” applies ruthlessly to AI. Data quality problems kill more AI projects than algorithm choices. Most failures trace back to data issues—not methodology.

⚠️ Critical Data Challenges:

Missing Data: Not random—missingness often correlates with outcome (sicker patients have incomplete records)
Label Quality: Ground truth is often noisy, biased, or inconsistent
Data Drift: Distributions change over time (COVID-19 changed everything)
Selection Bias: Your training data isn’t representative of deployment population
Measurement Errors: Sensors fail, humans make mistakes, systems have artifacts

✅ Data Quality Checklist:

Completeness: What’s missing and why?
Consistency: Same concepts coded differently across systems?
Temporal Validity: When was this recorded? Is it current?
Representativeness: Does training data match deployment population?
Label Quality: How was ground truth established? By whom?

💡 The 80/20 Rule of ML: 80% of effort is data preparation, 20% is modeling. Beginners reverse this ratio and fail.

🎯 Takeaway: Invest in data quality before fancy algorithms. Document data limitations explicitly. Assume your data has problems until proven otherwise.

→ See also: Chapter 8 (Evaluation), Chapter 10 (Privacy)

A.2 Part II: Current Applications

A.2.1 Chapter 4: Disease Surveillance and Outbreak Detection

💡 The Big Picture: AI can process vast surveillance data faster than humans, but cannot replace epidemiological judgment. Google Flu Trends failed. ProMED-mail (human curators) succeeded.

✅ What Works:

Syndromic surveillance: Automated detection of unusual patterns in emergency department data
Genomic surveillance: AI for variant identification and transmission tracking
Social media mining: Signal detection (not prevalence estimation)
Human-AI collaboration: Algorithms flag signals, epidemiologists investigate

❌ What Fails:

Pure algorithmic surveillance without domain expertise
Overfitting to historical patterns (COVID-19 broke all models)
Ignoring epidemiological context (correlation ≠ causation)
Replacing human judgment with automation

🎯 Takeaway: Use AI to augment, not replace, epidemiological surveillance. Algorithms detect signals—humans determine public health action.

→ See also: Chapter 5 (Forecasting), Chapter 6 (Genomics)

A.2.2 Chapter 5: Epidemic Forecasting

💡 The Big Picture: Epidemic forecasting is harder than weather forecasting. Models inform decisions but shouldn’t dictate policy. The goal is “usefulness” not “perfect accuracy.”

⚠️ Fundamental Challenges:

Limited data early in outbreaks when forecasts matter most
Human behavior changes predictions (reactive policies, behavioral responses)
Model uncertainty is large and often underestimated
Evaluation is difficult: Can’t rerun history to test counterfactuals

✅ What Works:

Ensemble models: Combine multiple approaches (beats individual models)
Scenario planning: “What if?” analysis rather than single point predictions
Short-term forecasts: 1-4 weeks ahead more reliable than long-term
Transparent uncertainty: Report prediction intervals, not just point estimates

💡 Key Insight: The COVID-19 Forecast Hub combined 50+ models and outperformed individual forecasts. Humility and ensemble thinking work.

🎯 Takeaway: Use forecasts for planning, not certainty. Report uncertainty explicitly. Combine models. Update frequently as data arrives.

→ See also: Chapter 4 (Surveillance), Chapter 11 (Safety)

A.2.3 Chapter 6: Genomic Surveillance and Pathogen Analysis

💡 The Big Picture: AI accelerates genomic analysis from weeks to hours. Critical for variant tracking, outbreak investigation, and antimicrobial resistance monitoring.

✅ Key Applications:

Variant identification: Detect new SARS-CoV-2 variants, influenza strains
Transmission tracking: Phylogenetic analysis for outbreak investigation
Antimicrobial resistance prediction: Predict resistance from genome sequences
Pathogen classification: Rapid identification for clinical decision support

⚠️ Critical Limitations:

Requires high-quality sequencing data (garbage in, garbage out)
Models trained on existing pathogens struggle with novel ones
Interpretation requires domain expertise (bioinformatics + epidemiology)
Equity concerns: Sequencing capacity concentrated in high-income countries

🎯 Takeaway: AI makes genomic surveillance scalable. Combine computational tools with epidemiological and laboratory expertise. Address equity gaps in sequencing capacity.

→ See also: Chapter 4 (Surveillance), Chapter 17 (Global Health)

A.2.4 Chapter 7: Diagnostic and Clinical Support

💡 The Big Picture: AI matches or exceeds human experts on narrow, well-defined tasks (diabetic retinopathy, some radiology). Fails at general clinical reasoning. Clinical deployment remains rare despite research hype.

✅ Success Stories:

Diabetic retinopathy screening: FDA-approved, deployed at scale
Radiology decision support: Detects fractures, lung nodules, specific patterns
Pathology: Cancer detection in histology slides
Specific tasks: Well-defined problems with clear ground truth

❌ Why Most Clinical AI Fails:

Generalization gap: Works in lab, fails in real clinics
Workflow integration: Doesn’t fit existing clinical processes
Alert fatigue: Too many false positives
Trust barriers: Clinicians don’t understand or trust “black box” predictions
Liability concerns: Who’s responsible when AI is wrong?

⚠️ Epic Sepsis Model Lesson: Deployed at 100+ hospitals, 33% sensitivity (missed 2 of 3 cases), 7% PPV (93% false alarms). Deployment ≠ effectiveness.

🎯 Takeaway: Demand external validation. Evaluate workflow integration early. Monitor real-world performance continuously. Narrow, well-defined problems work. General clinical AI remains aspirational.

→ See also: Chapter 1 (History), Chapter 8 (Evaluation), Chapter 12 (Deployment)

A.3 Part III: Implementation and Evaluation

A.3.1 Chapter 8: Evaluating AI Systems

💡 The Big Picture: Only 6% of medical AI studies perform external validation. Epic’s sepsis model—deployed at 100+ hospitals—had 33% sensitivity and 7% PPV in external validation. The gap between lab performance and real-world deployment kills promising AI systems.

The Evaluation Hierarchy:

Internal Validation: Weakest evidence—tells you if model memorized vs. learned
Temporal Validation: Better—checks if model works as time passes
External Validation: Critical—tests generalization
Prospective Validation: Deployed in real clinical workflow
Randomized Controlled Trial (RCT): Gold standard—proves clinical utility

Most papers stop at #1. Deployment requires #3-5.

❌ Common Evaluation Pitfalls:

No external validation
Cherry-picked subgroups
Ignoring prevalence shift
Overfitting to dataset quirks
Evaluation-treatment mismatch

✨ NEW for 2025—Evaluating Foundation Models & LLMs:

Factual accuracy, hallucination detection, prompt sensitivity
Medical benchmarks (but benchmarks ≠ clinical competence)
RAG evaluation: assess retrieval AND generation separately

✨ NEW for 2025—Continuous Monitoring:

Data drift, concept drift, label drift
Retraining triggers when performance degrades

⚠️ When NOT to Deploy:

Poor external validation
Systematic bias in fairness audit
Workflow integration causes harm (alert fatigue)
Users don’t trust the system
No monitoring plan

🎯 Takeaway: Evaluation is ongoing, not a checkbox. Most AI systems fail between internal and external validation. Don’t let Epic’s sepsis model be your model.

→ See also: Chapter 20 (LLM Evaluation), Chapter 9 (Ethics)

A.3.2 Chapter 9: Ethics, Bias, and Equity

💡 The Big Picture: AI systems inherit and often amplify existing societal biases. The Obermeyer algorithm showed Black patients had to be sicker than White patients to receive the same risk score—despite excellent technical accuracy.

⚠️ Sources of Bias:

Historical bias: Training data reflects past discrimination
Representation bias: Underrepresented groups in training data
Measurement bias: How outcomes are defined and measured
Aggregation bias: One model doesn’t fit all subgroups equally
Deployment bias: How systems are used differs from design intent

✅ Fairness Evaluation Framework:

Group fairness: Equal performance across demographic groups
Individual fairness: Similar individuals receive similar predictions
Calibration: Predictions equally accurate across groups
No fairness metric satisfies all definitions simultaneously (mathematical tradeoffs exist)

💡 Critical Insight: Technical performance ≠ ethical deployment. Must evaluate fairness explicitly alongside accuracy.

🎯 Takeaway: Bias audits are mandatory, not optional. Evaluate fairness across subgroups. Engage affected communities in design. Perfect fairness is mathematically impossible—make explicit tradeoffs.

→ See also: Chapter 8 (Evaluation), Chapter 10 (Privacy), Chapter 17 (Global Health)

A.3.3 Chapter 10: Privacy, Security, and Governance

💡 The Big Picture: Public health data is sensitive. Privacy violations destroy trust and violate regulations (HIPAA, GDPR). Most privacy problems are preventable with proper data governance.

⚠️ Privacy Risks:

Re-identification: “De-identified” data can often be re-identified
Inference attacks: Models leak training data information
Data breaches: Poor security practices
Secondary use without consent: Using data for purposes beyond original collection
Surveillance creep: Well-intentioned systems become surveillance infrastructure

✅ Privacy-Preserving Techniques:

Differential privacy: Mathematical guarantee of privacy protection
Federated learning: Train models without centralizing data
Synthetic data: Generate realistic but fake data for development
Secure multi-party computation: Analyze distributed data without sharing raw data

💡 Key Principle: Minimum necessary data. Collect only what’s needed, keep only as long as required, limit access strictly.

🎯 Takeaway: Privacy is not optional—it’s legal, ethical, and essential for trust. Implement privacy-by-design. Use technical safeguards. Establish clear data governance.

→ See also: Chapter 9 (Ethics), Chapter 18 (Policy)

A.3.4 Chapter 11: AI Safety: Protecting Patients and Populations

💡 The Big Picture: AI systems can fail in unexpected ways. Safety requires proactive risk assessment, continuous monitoring, and organizational preparedness for failures.

⚠️ Safety Risks:

Distribution shift: Model encounters data unlike training (most common failure mode)
Adversarial inputs: Intentionally crafted to fool models
Cascading failures: AI error triggers downstream problems
Automation bias: Humans overtrust AI and stop critical thinking
Silent failures: System degrades gradually without obvious signals

✅ Safety Framework:

Pre-deployment: Rigorous testing, failure mode analysis, red team testing
During deployment: Human oversight, confidence thresholds, alerting systems
Post-deployment: Continuous monitoring, incident reporting, iterative improvement
Organizational: Clear accountability, fallback procedures, staff training

💡 Key Insight: Plan for failure, not just success. Every AI system will eventually fail—how quickly can you detect and respond?

🎯 Takeaway: Safety is not a feature—it’s a system property requiring technical, human, and organizational components. Monitor continuously. Plan fallbacks. Train staff.

→ See also: Chapter 8 (Evaluation), Chapter 12 (Deployment)

A.3.5 Chapter 12: Deployment, Monitoring, and Maintenance

💡 The Big Picture: Deployment is where AI projects succeed or fail. Technical performance in lab ≠ clinical utility in practice. Most failures are organizational, not technical.

⚠️ Common Deployment Failures:

Workflow mismatch: AI doesn’t fit existing clinical processes
Alert fatigue: Too many false positives, users ignore warnings
Lack of trust: Clinicians don’t understand or accept recommendations
No monitoring plan: Performance degrades silently
No maintenance plan: Who updates the model? When? How?

✅ Deployment Checklist:

Before launch: Pilot testing, workflow integration, staff training, fallback procedures
During launch: Gradual rollout, intensive monitoring, user feedback loops
After launch: Continuous performance monitoring, regular audits, planned updates

💡 The 80/20 Rule of AI Projects: 20% building model, 80% deployment and maintenance. Beginners reverse this and fail.

🎯 Takeaway: Deployment is not the end—it’s the beginning. Plan for maintenance from day one. Monitor continuously. Engage users throughout.

→ See also: Chapter 11 (Safety), Chapter 8 (Evaluation), Chapter 7 (Clinical AI)

A.4 Part IV: Practical Tools and Resources

A.4.1 Chapter 13: Your AI Toolkit

💡 The Big Picture: You don’t need expensive software or infrastructure to start. Python ecosystem provides free, open-source tools for most public health AI applications.

✅ Essential Tools:

Python: Industry standard for AI/ML (use Anaconda distribution)
Jupyter Notebooks: Interactive development and documentation
Core libraries: pandas (data), scikit-learn (ML), matplotlib/seaborn (visualization)
Deep learning: PyTorch or TensorFlow (only if needed)
Version control: Git + GitHub (non-negotiable for reproducibility)

💡 Golden Rule: Master the basics before adding complexity. Excel → pandas → scikit-learn → deep learning. Most practitioners stop at scikit-learn and that’s fine.

❌ Common Mistakes:

Starting with deep learning instead of simpler approaches
Tool hopping before mastering fundamentals
Ignoring version control (“final_model_v3_FINAL_actually_final.py”)
No reproducibility (can’t recreate results 6 months later)

🎯 Takeaway: Start simple. Master pandas and scikit-learn—these solve 80% of problems. Use version control from day one. Cloud computing is optional initially.

→ See also: Chapter 14 (First Project), Chapter 15 (AI-Assisted Coding)

A.4.2 Chapter 14: Building Your First Project

💡 The Big Picture: Best way to learn AI is by doing. Start with a complete project (data → model → evaluation) rather than abstract tutorials.

✅ Project Workflow:

Define problem clearly: What are you predicting? Why does it matter?
Get data: Start with public datasets (UCI, Kaggle, CDC WONDER)
Exploratory data analysis: Visualize, find patterns, identify issues
Feature engineering: Create meaningful variables using domain knowledge
Model building: Start simple (logistic regression), add complexity if needed
Evaluation: Multiple metrics, cross-validation, external validation if possible
Documentation: Code comments, README, results interpretation

💡 Key Insight: Your domain expertise is your advantage. Feature engineering matters more than algorithm choice.

🎯 Takeaway: Pick a small, well-defined problem. Complete it end-to-end. Document everything. Learn by doing, not just reading.

→ See also: Chapter 13 (Toolkit), Chapter 2 (AI Basics), Chapter 8 (Evaluation)

A.4.3 Chapter 15: AI-Assisted Coding and Development Tools

💡 The Big Picture: AI coding assistants (GitHub Copilot, Cursor, ChatGPT) dramatically accelerate development. Use them to write faster, not think less.

✅ Effective Use Cases:

Boilerplate code: Data loading, preprocessing, standard plots
Syntax lookup: “How do I [X] in pandas?”
Debugging: Explain error messages, suggest fixes
Documentation: Generate docstrings, README files
Learning: Explore new libraries, understand code examples

❌ Pitfalls:

Blindly accepting suggestions without understanding
Using AI-generated code without testing
Letting AI make methodological decisions (that’s your job)
Ignoring hallucinations (AI confidently generates wrong code)

💡 Key Principle: AI is a junior developer—fast but needs supervision. Review all code. Understand before using. Make scientific decisions yourself.

🎯 Takeaway: AI assistants are productivity multipliers, not replacements for knowledge. Use them to write faster, learn faster, debug faster. But you still need to understand the code.

→ See also: Chapter 13 (Toolkit), Chapter 20 (LLM Theory and Practice)

A.5 Part V: The Future

A.5.1 Chapter 16: Emerging Technologies

💡 The Big Picture: Foundation models, multimodal AI, and explainable AI represent the frontier. Some are hype, some are genuine advances. Separate signal from noise.

✨ Genuinely Promising:

Foundation models: Pre-trained models adaptable to many tasks (but require validation)
Multimodal AI: Combine images, text, clinical data (promising for complex diagnostics)
Federated learning: Privacy-preserving collaborative training across institutions

⚠️ Mostly Hype (for now):

Quantum ML: Years away from practical public health applications
AGI for healthcare: General medical AI remains science fiction
Blockchain for health data: Solution looking for a problem

💡 Key Insight: Emerging technologies are tools, not solutions. Evaluate based on whether they solve your specific problem, not novelty.

🎯 Takeaway: Stay informed but skeptical. Proven methods often outperform cutting-edge ones. Adopt new tech when it solves real problems better than alternatives.

→ See also: Chapter 20 (LLMs), Chapter 17 (Global Health)

A.5.2 Chapter 17: Global Health and Equity

💡 The Big Picture: AI has potential for global health but risks amplifying existing inequities. Most AI development happens in high-income countries using high-income country data.

⚠️ Equity Challenges:

Data desert: Low- and middle-income countries (LMICs) underrepresented in training data
Infrastructure gaps: Limited computing, internet, electricity in many settings
Resource mismatch: AI optimized for high-tech hospitals doesn’t work in resource-limited clinics
Brain drain: AI talent concentrated in tech companies, not global health
Digital colonialism: Solutions imposed rather than co-created with communities

✅ Equity-Centered Approaches:

Co-design with affected communities from the start
Appropriate technology: Simple, robust, works offline, low-power
Local capacity building: Train local teams, don’t just import solutions
Open source and open data: Reduce barriers to entry
Validation in target populations: Don’t assume generalization

🎯 Takeaway: AI for global health requires intentional equity focus. Co-create, don’t impose. Validate in target populations. Build local capacity.

→ See also: Chapter 9 (Ethics), Chapter 18 (Policy)

A.5.3 Chapter 18: Policy and Governance

💡 The Big Picture: AI regulation is evolving rapidly. FDA, EU AI Act, WHO guidelines all shaping requirements. Even non-regulated systems benefit from regulatory-level rigor.

✨ Key Regulatory Frameworks:

FDA SaMD: Software as Medical Device—risk-based classification (I, II, III)
EU AI Act: High-risk medical AI requires conformity assessment, transparency, human oversight
WHO guidance: Ethical principles for AI in health
Good Machine Learning Practice (GMLP): Industry standards for development and validation

💡 Core Principles Across Frameworks:

Transparency: How does system work? What are limitations?
Accountability: Who’s responsible when AI fails?
Safety: Proactive risk management, continuous monitoring
Equity: Fair performance across populations
Human oversight: AI augments, doesn’t replace, human judgment

🎯 Takeaway: Regulation is complex but directionally consistent: safety, transparency, fairness, human oversight. Apply these principles even to non-regulated systems.

→ See also: Chapter 8 (Evaluation), Chapter 11 (Safety), Chapter 9 (Ethics)

A.5.4 Chapter 19: AI, Misinformation, and the Infodemic

💡 The Big Picture: AI enables misinformation at scale (deepfakes, synthetic text) but also helps combat it (detection, fact-checking). The arms race is ongoing.

⚠️ Threats:

Deepfakes: Synthetic images/videos of public health officials spreading false information
LLM-generated disinfo: ChatGPT creates convincing but false health content at scale
Micro-targeted manipulation: Personalized misinformation based on psychological profiles
Bot networks: AI-driven social media accounts amplify false narratives

✅ Countermeasures:

Detection tools: AI to identify synthetic content, bot behavior
Fact-checking augmentation: AI to assist human fact-checkers (not replace)
Prebunking: Proactively educate public about manipulation techniques
Platform accountability: Pressure social media companies to reduce algorithmic amplification

💡 Key Insight: Technical solutions alone insufficient. Need media literacy, platform accountability, public health communication strategy.

🎯 Takeaway: AI is both problem and solution for health misinformation. Combine technical detection with human judgment, media literacy, and communication strategy.

→ See also: Chapter 20 (LLMs), Chapter 21 (Behavioral AI)

A.5.5 Chapter 20: Large Language Models in Public Health: Theory and Practice

💡 The Big Picture: LLMs (ChatGPT, Claude, Med-PaLM) show promise for clinical documentation, patient communication, literature review—but have serious limitations. Hallucinations, lack of reasoning, privacy risks.

✅ Promising Use Cases:

Clinical documentation: Summarize notes, generate drafts (with human review)
Patient communication: Answer common questions, health education
Literature review: Summarize research, extract key findings
Data analysis assistance: Generate code, explain results
Translation: Medical information across languages

⚠️ Critical Limitations:

Hallucinations: Confidently generate false medical information
No true reasoning: Pattern matching, not understanding
Training data biases: Reproduce societal and medical biases
Privacy risks: Sensitive data sent to third-party APIs
Lack of accountability: Who’s responsible for LLM errors?

✅ Safe Deployment Practices:

Human-in-the-loop: Never fully automate clinical decisions
Retrieval-Augmented Generation (RAG): Ground LLM outputs in verified sources
Fact-checking: Verify all medical claims before use
Privacy protection: Never send patient data to public LLM APIs
Continuous evaluation: Monitor for hallucinations, bias, drift

🎯 Takeaway: LLMs are powerful but flawed tools. Use for augmentation, not replacement. Validate outputs. Protect privacy. Never fully automate clinical decisions.

→ See also: Chapter 8 (Evaluation), Chapter 10 (Privacy), Chapter 15 (AI Coding)

A.5.6 Chapter 21: AI-Driven Behavioral Interventions

💡 The Big Picture: AI enables personalized behavior change interventions at scale (chatbots, adaptive messaging, digital nudges). Promising for public health but raises ethical concerns about manipulation.

✅ Applications:

Health coaching chatbots: Medication adherence, diet, exercise
Personalized messaging: Tailored health communication based on individual characteristics
Just-in-time interventions: Context-aware prompts (e.g., smoking cessation when stress detected)
Social norm nudges: Behavioral feedback using peer comparisons

⚠️ Ethical Concerns:

Manipulation vs. persuasion: Where’s the line?
Autonomy: Does personalization undermine informed choice?
Equity: Does AI-driven behavior change widen health disparities?
Privacy: Requires extensive personal data collection
Effectiveness uncertainty: Many digital health apps lack rigorous evaluation

💡 Key Principle: Transparency and user control. People should understand how personalization works and be able to opt out.

🎯 Takeaway: AI-driven behavioral interventions show promise but require ethical guardrails. Ensure transparency, respect autonomy, evaluate effectiveness rigorously.

→ See also: Chapter 9 (Ethics), Chapter 10 (Privacy), Chapter 20 (LLMs)

A.6 How to Use This Reference

By Role:

Policymakers/Directors: Read summaries for Parts III (Implementation) and V (Future) to inform strategic decisions
Epidemiologists/Practitioners: Focus on Parts II (Applications) and IV (Practical Tools) for applied knowledge
Developers/Data Scientists: Study Parts I (Foundations), III (Implementation), and IV (Practical Tools) for technical depth
Students: Read sequentially for comprehensive understanding, then return to specific summaries as reference

By Need:

Evaluating vendor AI products: Chapters 8, 11, 12 (Evaluation, Safety, Deployment)
Addressing fairness concerns: Chapters 9, 17 (Ethics, Global Health)
Starting first AI project: Chapters 2, 3, 13, 14 (Basics, Data, Toolkit, First Project)
Understanding LLMs (ChatGPT, etc.): Chapters 15, 19, 20 (AI Coding, Misinformation, LLM Theory/Practice)

Final Note: These summaries represent hundreds of hours of synthesis. They’re designed to be sufficient for 80% of decision-making. When you need the remaining 20%, dive into the full chapters for implementation details, code examples, and comprehensive case studies.

Last updated: 2025 Full handbook: https://publichealthaihandbook.com