1AI Fundamentals for Clinical Researchers

What Is Artificial Intelligence? A Clinician's Primer
Supervised, Unsupervised, and Self-Supervised Learning
Common AI Architectures in Medicine
Foundation Models and Large Language Models in Clinical Research
Matching the Problem to the Approach
What AI Cannot Do: Limitations and Common Misconceptions

2Data: The Foundation of Medical AI

Why Data Quality Matters More Than Model Complexity
How Much Data Do You Need?
Data Annotation and Labeling for Clinical AI
Class Imbalance: When Rare Conditions Dominate Your Research Question
Data Preprocessing and Augmentation
Multi-Site and Multi-Device Data: The Generalization Challenge
De-Identification, Privacy, and Regulatory Compliance for Research Data
Synthetic Data and Data Simulation

3Developing AI Models

The AI Development Lifecycle: From Hypothesis to Deployment
Training, Validation, and Test Sets: Why You Need All Three
Transfer Learning: Standing on the Shoulders of Large Models
Hyperparameter Tuning and Model Selection
Overfitting, Underfitting, and Regularization
Working with an Engineering Team: The Clinician's Role
Tools and Infrastructure: What You Need to Know

4Evaluating Model Performance

5Clinical Validation and Study Design

Technical Validation vs. Clinical Validation: Understanding the Difference
Designing Retrospective Validation Studies
Designing Prospective Clinical Validation Studies
Multi-Site External Validation
Reader Studies: Measuring AI's Impact on Clinical Decision-Making
Defining Clinically Meaningful Endpoints
Reference Standards and Ground Truth in Medical AI

6Regulatory Pathways for Medical AI

7Bias, Fairness, and Responsible AI

Sources of Bias in Medical AI
Demographic Fairness: Performance Across Populations
Generalization Across Devices, Sites, and Geographies
Explainability and Interpretability in Clinical AI
Transparency and Reproducibility
Ethical Frameworks for AI in Healthcare

8Implementation and Clinical Workflow Integration

From Model to Product: The Implementation Gap
Clinical Workflow Analysis: Where Does AI Fit?
Interoperability: DICOM, HL7 FHIR, and Integration Standards
Human-AI Interaction and User Interface Design
Clinician Trust, Adoption, and Change Management
Monitoring AI Performance in Production

9Publishing and Reporting AI Research

Reporting Standards: CONSORT-AI, SPIRIT-AI, TRIPOD+AI, and STARD
Writing the Methods Section for an AI Study
Common Methodological Pitfalls in Medical AI Papers
Sharing Code, Data, and Models
Peer Review of AI Manuscripts: What Reviewers Look For

AAppendices

Glossary of AI and Machine Learning Terms for Clinicians
Recommended Reading and Courses
Regulatory Standards Quick Reference
Checklist: Is Your AI Study Ready for Publication?

Implementation and Clinical Workflow Integration

Getting an AI tool from the lab into clinical practice. This section covers the practical, technical, and human factors that determine whether a validated model actually improves care.

8.1

From Model to Product: The Implementation Gapsoon

Why most published AI models never reach clinical practice. The chasm between research performance and real-world deployment, and what it takes to cross it.

8.2

Clinical Workflow Analysis: Where Does AI Fit?soon

Methods for analyzing existing clinical workflows, identifying the right insertion point for AI, and designing interactions that augment rather than disrupt the clinician's process.

8.3

Interoperability: DICOM, HL7 FHIR, and Integration Standardssoon

The technical standards that govern how AI tools exchange data with imaging systems, electronic health records, and clinical infrastructure. What researchers need to know about DICOM and FHIR.

8.4

Human-AI Interaction and User Interface Designsoon

How the presentation of AI results affects clinical decision-making. Alert fatigue, anchoring bias, automation complacency, and evidence-based principles for designing effective AI interfaces.

8.5

Clinician Trust, Adoption, and Change Managementsoon

The human factors that determine whether clinicians will actually use an AI tool. Building trust through transparency, managing expectations, training strategies, and lessons from implementation science.

8.6

Monitoring AI Performance in Productionsoon

How to detect degradation after deployment: data drift, concept drift, performance monitoring dashboards, and establishing feedback loops between clinical users and development teams.