The AI Product Playbook Strategies, Skills, and Frameworks for the AI-Driven Product Manager (Marily Nika, Diego Granados) (z-library.sk, 1lib.sk, z-lib.sk)

The AI Product Playbook D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

The AI Product Playbook Strategies, Skills, and Frameworks for the AI- Driven Product Manager Dr. Marily Nika Diego Granados D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

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v About the Authors Dr. Marily Nika is an award- winning GenAI product leader at Google and one of the world’s foremost AI educators, with over 13 years of experience building AI products at Google and Meta. She holds a PhD in machine learning and is an author, TED AI speaker, Harvard Business School fellow and co- founder of the AI Product Hub that offers AI product management courses at aiproduct.com. Originally from Mexico and now based in Seattle, Diego Granados is an AI Product Manager at Google Cloud, where he focuses on building the products and tools that empower AI developers and data scientists. With a unique background that combines an MBA from Duke University’s Fuqua School of Business and an MS in Computer Science with a focus in AI from Georgia Tech, Diego has built his career on bridging the gap between business strategy and deep technical execution. He believes this combination is a PM’s superpower in the age of AI, allowing for better communication, strategy, and a true understanding of the entire data science lifecycle. D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

vii Acknowledgments I would like to thank my husband, Ray, for his encouragement throughout my journey as an author and educator. His belief in me and this project made the long hours truly purposeful. I am also grateful to the mentors, peers, and colleagues in the AI product management communities who inspired and challenged me to think deeply and critically. Your conversations, feedback, and insights helped shape the direction of this book. Finally, to our readers, current students and alumni of the AI Product Hub— thank you for your curiosity, your passion for building meaningful AI products, and your commitment to learning. — Marily To my wife, Daniela. While my name is on the cover, her support is on every page. She is the reason I’ve gotten so far in my career, and her part- nership is what created the space for me to pursue the work that made this book possible. For her constant belief in me, I am eternally grateful. — Diego D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Contents at a Glance Introduction xix Part I Foundational AI/ML Concepts 1 Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know 3 Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood 19 Chapter 3 The Big Picture: AI, ML, and You 41 Chapter 4 The AI Lifecycle 101 Part II AI PM Specializations 111 Chapter 5 AI-Experiences PM: Shaping User Interaction with AI 113 Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems 137 Chapter 7 AI-Enhanced PM: Supercharging Product Management with AI 159 Part III Connecting the Dots Between AI/ML Knowledge and PM Craft 187 Chapter 8 Identifying and Evaluating AI Opportunities 189 Chapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value 229 ix D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Chapter 10 Building and Deploying AI Solutions: From Lab to Live 261 Chapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity 281 Chapter 12 Conclusion: Paving Your Own Path to AI PM 301 Index 307   x Contents at a Glance D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Contents Introduction xix Part I Foundational AI/ML Concepts 1 Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know 3 AI vs. ML 4 Why This Matters to a PM 4 Key Differences Between AI and ML 5 Common Misconceptions for PMs: Myths vs. Reality 7 Your Glossary as a PM 7 Grounding the Concepts: Real-World AI in Action 10 The AI PM’s Guiding Principles 14 Chapter Summary and Key Takeaways 16 Key Takeaways 16 Onward: Peeking Under the Hood 17 Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood 19 The Learning Process: Training, Validation, and Testing 20 How Models Learn: An Example with k-Nearest Neighbors (k-NN) 22 Applying k-NN (with k=1): 23 Another Example: Testing an Unknown Fruit 26 Evaluating Model Performance 27 The Confusion Matrix: A Foundation for Understanding 27 xi D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Key Classification Metrics (and Their PM Implications) 28 The Precision-Recall Trade-Off 29 Choosing the Right Metric 30 Overfitting and Underfitting: Striking the  Right Balance for Real-World Performance 31 Overfitting: Memorizing Instead of Learning 31 Underfitting: Missing the Forest for the Trees 32 Visual Analogy: Fitting a Curve 32 Finding the Sweet Spot: Generalization 33 The PM’s Role 33 Human-in-the-Loop: Blending AI Power with Human Expertise 34 What Is Human-in-the-Loop? 34 Why HITL Is Essential for Product Managers (and Their Products) 35 How to Implement HITL (PM Considerations) 37 Chapter Summary and Key Takeaways 38 Key Takeaways 39 Onward: Understanding the Broader Process 39 Chapter 3 The Big Picture: AI, ML, and You 41 Understanding the Relationship Between AI, ML, and Product Goals 41 Types of Machine Learning: Understanding the Spectrum of Learning 44 Supervised Learning: Guiding the Model with Labeled Examples 46 Technical Deep Dive: How Supervised Learning Models Learn from Labeled Data 48 Critical Considerations for Product Managers 54 Unsupervised Learning: Discovering Hidden Patterns in Your Data 55 Technical Deep Dive: How Unsupervised Learning Models Discover Patterns 57 Critical Considerations for Product Managers 60 Reinforcement Learning: Learning Through Trial and Error 61 Technical Deep Dive: How Reinforcement Learning Agents Learn Optimal Policies 63 The Learning Process: Exploration, Exploitation, and Q-Learning 65 Critical Considerations for Product Managers 67 Generative AI: Powering a New Era of Language-Based Applications 67 Technical Deep Dive: How LLMs Understand and Generate Language 69 xii Contents D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Contents xiii Critical Considerations for Product Managers 72 The “Gotchas”: A PM’s Guide to LLM Limitations and Risks 73 Navigating the Nuances of Generative AI: Understanding GenAI Evaluations— Ensuring Quality and Trust 75 Prompt Engineering: The Art and Science of Talking to AI 84 Types of Machine Learning: A Recap 89 Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition 92 Neural Networks: Mimicking the Brain’s Connections (But Not Really) 92 How Neural Networks Learn: Adjusting the Connections 94 Technical Deep Dive: The Mechanics of  Neural Networks and Deep Learning 95 Challenges in Deep Learning 98 Chapter Summary and Key Takeaways 99 Key Takeaways 99 Onward: Mapping the Process 100 Chapter 4 The AI Lifecycle 101 Problem Definition and Business Understanding: The “Why” 102 Data Collection and Exploration: Understanding Your Ingredients 103 Data Preprocessing: Preparing the Ingredients 104 Feature Engineering: Crafting the Inputs for Success 104 Model Selection and Training: Choosing the Right Algorithm 105 Model Evaluation and Tuning: Ensuring Quality 106 Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) 107 Retraining and Maintenance: Keeping Your Model Up-to-Date 108 Chapter Summary and Key Takeaways 109 Key Takeaways 109 Onward: Exploring the AI PM Roles 110 Part II AI PM Specializations 111 Chapter 5 AI-Experiences PM: Shaping User Interaction with AI 113 Key Responsibilities: Shaping the AI User Experience 114 Day-to-Day Activities 117 D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

xiv Contents Required Skills and Knowledge: The AI-Experiences PM Toolkit 120 Core Product Management Craft and Practices 120 Engineering Foundations for PMs 121 Essential Leadership and Collaboration Skills 122 AI Lifecycle and Operational Awareness 123 Illustrative Example: A Day in the Life of an AI-Experiences PM 124 Challenges and Complexities 127 How the AI-Experiences PM Interacts with Other Roles 129 Chapter Summary and Key Takeaways 134 Key Takeaways 134 Onward: Architecting the AI Foundation 135 Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems 137 Key Responsibilities: Building and Managing the AI Foundation 138 Day-to-Day Activities 141 Required Skills and Knowledge: The AI-Builder PM’s Technical and Strategic Toolkit 144 Core Product Management Craft and Practices 145 Engineering Foundations for PMs 146 Essential Leadership and Collaboration Skills 147 AI Lifecycle and Operational Awareness 148 Illustrative Example: A Day in the Life of an AI-Builder PM 149 Challenges and Complexities 152 How the AI-Builder PM Interacts with Other Roles 154 Chapter Summary and Key Takeaways 156 Key Takeaways 157 Onward: Supercharging the PM Workflow 158 Chapter 7 AI-Enhanced PM: Supercharging Product Management with AI 159 Key Responsibilities: Augmenting PM Workflows and Decision-Making with AI 160 Day-to-Day Activities 162 Required Skills and Knowledge: The AI-Enhanced PM’s Toolkit 165 Core Product Management Craft and Practices 165 Engineering Foundations for PMs 166 Essential Leadership and Collaboration Skills 167 AI Lifecycle and Operational Awareness 168 D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Contents xv Illustrative Example: A Day in the Life of an AI-Enhanced PM 169 Examples of AI Tools 172 Challenges and Complexities 173 How the AI-Enhanced PM Interacts with Other Roles 175 Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM 177 Chapter Summary and Key Takeaways 184 Key Takeaways 185 Onward: From Theory to Action 185 Part III Connecting the Dots Between AI/ML Knowledge and PM Craft 187 Chapter 8 Identifying and Evaluating AI Opportunities 189 Uncovering Potential Use Cases—Mining Your Product for AI Gold 189 Recognizing Data-Rich Problem Areas 190 Analyzing Existing Data Sources 192 Asking the Right Questions 193 AI/ML Capability Matching: Connecting Problems to Solutions 194 Understanding Your AI/ML Toolkit: Key Capabilities 195 Matching Capabilities to Problems: A  Practical Approach 200 Feature: Search Functionality in a Document Management System 200 Feature: Customer Support Chatbot 201 Feature: Reporting Dashboard for Marketing Campaigns 201 Finding AI Opportunities in the User Journey 202 Mapping the User Journey: Charting the Course 202 Identifying Pain Points and Opportunities: The AI Detective Work 204 Applying AI/ML to Enhance Touchpoints: The Transformation 205 Feature Enhancement Through AI/ML— Transforming Existing Functionality 208 Identifying Enhancement Opportunities: Finding the Weak Spots 209 Applying AI/ML to Enhance Features: The Transformation Process 210 Feature: Standard Search Functionality 212 Feature: Data Entry Form 212 Feature: Reporting Dashboard 212 D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

xvi Contents Proactive Product Management—Anticipating User Needs with AI 213 Understanding the Power of Prediction and Automation 213 Key Areas for Predictive and Automation Opportunities 214 Identifying Opportunities: A Practical Approach 216 Responsible AI Foundations—Ethical and Feasibility Considerations 217 Ethical Considerations: The “Do No Harm” Principle 217 Feasibility Considerations: Can We Actually Build This? 220 Practical Ideation Techniques for AI/ML Use Cases—Thinking Like an AI-First Product Manager 221 Ideation Techniques: Unleashing Your AI Creativity 222 “AI Feature Storming”: The Brain Dump 222 “AI Scenario Planning”: Walking in the User’s Shoes 223 “Data Opportunity Mapping”: Leveraging Your Data Assets 223 “AI Capability Alignment”: The Matching Game 224 “AI-Powered Feature Reverse Engineering”: Learning from Others 225 Cultivating an AI-First Mindset 226 Chapter Summary and Key Takeaways 226 Key Takeaways 227 Onward: Measuring the Value of Your Ideas 227 Chapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value 229 From Model Performance to Business Impact: A PM’s Guide to AI Metrics 229 Defining AI/ML-Specific Metrics: The Foundation for Measuring ROI 230 The Importance of Baselines: Knowing Where You Started 230 Understanding the Confusion Matrix: Decoding Classification Performance 231 Key Performance Metrics for AI/ML Models: Beyond the Confusion Matrix 233 Context Matters: Selecting the Right Metrics for Your AI/ML Application 237 1. Define Your Business Goals (and Connect Them to User Needs) 237 D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Contents xvii 2. Consider the Type of AI/ML Application (and Its Inherent Trade-Offs) 238 3. Evaluate the Cost of Errors: The Risk Assessment 239 4. Translate Technical Metrics into Business Impact 240 Important Considerations 240 End-to-End Example—Predicting Churn in a Subscription Service 241 1. Identify the Business Goal: Defining the “Why” 241 2. Define the AI/ML Application and Solution 242 3. Identify Data Sources and Engineer Features: The Raw Materials 243 Available Data 243 Feature Engineering 243 The Product Manager’s Role in This Stage 244 4. Select the Metrics: Defining Success 245 The Cost of Errors: Prioritizing What Matters 245 Our Chosen Metrics 246 5. Establish Baseline Metrics: Setting the Starting Point 246 6. Conduct Model Training and Evaluation: Building and Testing the AI 247 7. Conduct A/B Testing: Measuring Real-World Impact 248 8. Calculate the Results and ROI: Quantifying the Value 248 Translating Results into Business Impact 249 Monitoring for Long-Term Success 249 9. Monitor and Maintain the Model for Long-Term Success 250 A/B Testing for AI and ML Projects: Validating Impact and Optimizing Performance 251 What Is A/B Testing (in a Nutshell)? 251 Why Is A/B Testing Especially Important for AI/ML? 252 How to Conduct A/B Testing for AI and ML: A Step-by-Step Guide 253 Key Considerations for AI/ML A/B Testing 258 Chapter Summary and Key Takeaways 259 Key Takeaways 259 Onward: From the Lab to a Live Product 260 Chapter 10 Building and Deploying AI Solutions: From Lab to Live 261 MLOps: The Key to Reliable and Scalable AI 261 Key Components of MLOps—The AI Production Line 264 CI/CD, IaC, and Collaboration: The Foundational Pillars of MLOps 271 Glossary of Key MLOps Terms 272 D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

xviii Contents MLOps End-to-End Example: Churn Prediction in a Subscription Service (Product Manager’s Perspective) 274 Chapter Summary and Key Takeaways 278 Key Takeaways 278 Onward: Building with Integrity 279 Chapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity 281 Understanding AI Bias and Fairness: The Foundation of Responsible AI 281 Identifying Potential Biases: Where Bias Can Creep In 282 Mitigating Potential Biases: A Proactive Approach 285 Protected Classes and AI Fairness— Designing for Inclusion 287 What are Protected Classes? 287 Why Focus on Protected Classes? (The Legal and Ethical Imperative) 287 How Protected Classes Relate to AI Bias: The Mechanisms of Discrimination 288 Mitigating Bias Related to Protected Classes: Actionable Steps for PMs 289 AI Ethics and Legal Compliance—From Principles to Practice 291 Understanding the Ethical Landscape: Core Principles 291 Understanding the Legal Landscape: Key Regulations 292 Actionable Steps for Product Managers: Building Ethically and Legally Compliant AI 293 Engaging with the Community and External Stakeholders 297 Chapter Summary and Key Takeaways 298 Key Takeaways 298 Onward: Paving Your Path 299 Chapter 12 Conclusion: Paving Your Own Path to AI PM 301 Embrace Lifelong Learning: Stay Curious and Iterative 302 Cultivate a User-Centric AI Mindset 303 Deepen Cross-Functional Collaboration Skills 303 Build a Distinct AI Portfolio (Show, Don’t Just Tell) 304 Develop a Personal Vision for Your AI Career 305 Keep Resilience and Adaptability at the Core 305 Final Thoughts 306 Index 307   D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

xix Introduction This playbook is the culmination of our combined experience working at the forefront of AI innovation at companies like Google and Microsoft, and from teaching and mentoring thousands of Product Managers. Over the years, we’ve seen brilliant technical teams build powerful AI models, only to see the resulting products fail to connect with users. We’ve also seen product leaders struggle to navigate the hype, intimidated by the jargon and uncertain how to bridge the gap between a business problem and a technical AI solution. This new landscape demands a new kind of product manager. This playbook was born from our passion to define and support that evolu- tion. It is built on the belief that the most successful PMs will be those who can blend timeless product craft with a modern understanding of AI, enabling them to guide their teams and products through the most significant technological shift of our generation. This book is your hands- on guide to developing that blend of skills. It’s designed for the non- technical professional, cutting through the hype to focus on actionable frameworks and real- world application. To help you navigate this journey, we have structured the book into three parts: ■ Part I: Foundational AI/ML Concepts will build your core literacy. We will demystify the essential concepts of AI and Machine Learning, from how models are trained and evaluated to the dif- ferent types of ML and the end- to- end data science lifecycle. D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

xx Introduction ■ Part II: AI PM Specializations will help you find your place. We will deep- dive into the three key PM personas— AI Experiences, AI Builder, and AI- Enhanced— so you can understand your strengths and chart a clear career path. ■ Part III: Connecting the Dots Between AI/ML Knowledge and PM Craft will show you how to put it all into practice. You’ll learn frameworks for identifying AI opportunities, calculating ROI, navigating ethical challenges, and deploying real- world AI solutions. This playbook is your roadmap. Let’s get started. D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

Foundational AI/ML Concepts Before diving into the unique AI Product Manager roles in the next sec- tion, it’s essential to first build a strong foundation in the concepts that power modern product experiences. This part of the book is designed to demystify the core of AI and Machine Learning from a Product Man- ager’s perspective. Our goal isn’t to turn you into a data scientist, but to equip you with the essential knowledge to collaborate effectively, evaluate feasibility, and lead with confidence in the AI era. In Part I, you will learn to: ■ Distinguish between AI, ML, and Deep Learning, and understand why those differences matter to your product strategy. ■ Recognize the four primary types of machine learning— supervised, unsupervised, reinforcement, and generative— with real product examples to ground the theory. ■ Grasp the fundamentals of how models are trained, validated, and tested, including critical concepts like overfitting, bias, and the role of Human- in- the- Loop (HITL). ■ Visualize the end- to- end Data Science Lifecycle, understanding the PM’s role at each stage. Par t I The AI Product Playbook: Strategies, Skills, and Frameworks for the AI-Driven Product Manager. Marily Nika and Diego Granados. © 2026 John Wiley & Sons, Inc. Published 2026 by John Wiley & Sons, Inc. D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense

By the end of Part I, you won’t just recognize technical terminology— you’ll understand how to apply it to your product work and be ready to bridge the gap between technical complexity and user value. 2 Part I ■ Foundational AI/ML Concepts D ow nloaded from https://onlinelibrary.w iley.com /doi/ by alex jounh - O regon H ealth & Science U niversity , W iley O nline L ibrary on [06/10/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense