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?

AI Fundamentals for Clinical Researchers

An introduction to artificial intelligence concepts, written for clinicians rather than engineers. This section answers the question: what is AI, and what can it do for my research?

1.1

What Is Artificial Intelligence? A Clinician's Primersoon

Demystifying AI, machine learning, and deep learning — what each term means, how they relate, and why the distinctions matter for clinical applications.

1.2

Supervised, Unsupervised, and Self-Supervised Learningsoon

The three core learning paradigms, explained through clinical examples: classification from labeled data, pattern discovery in unlabeled data, and foundation models trained on massive datasets.

1.3

Common AI Architectures in Medicinesoon

A conceptual overview of convolutional neural networks (CNNs), transformers, recurrent networks, and U-Nets — what each is good at and where you will encounter them in medical literature.

1.4

Foundation Models and Large Language Models in Clinical Researchsoon

What GPT, Med-PaLM, and similar models are, how they differ from purpose-built clinical models, and where they fit (and don't fit) in clinical workflows.

1.5

Matching the Problem to the Approachsoon

A decision framework: given a clinical question (classification, segmentation, prediction, natural language extraction, anomaly detection), which AI approach is most appropriate?

1.6

What AI Cannot Do: Limitations and Common Misconceptionssoon

Realistic expectations for AI in medicine — the gap between headline results and clinical reality, why "AI will replace doctors" is wrong, and the problems AI is genuinely bad at.