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?

Publishing and Reporting AI Research

Standards and best practices for communicating AI research to the clinical community. This section helps researchers produce publications that are rigorous, reproducible, and useful.

9.1

Reporting Standards: CONSORT-AI, SPIRIT-AI, TRIPOD+AI, and STARDsoon

The major reporting guidelines for AI clinical studies and diagnostic accuracy studies. What each standard requires, when each applies, and how to use them as a checklist during study design — not just at manuscript submission.

9.2

Writing the Methods Section for an AI Studysoon

What must be documented: data sources, inclusion/exclusion criteria, preprocessing, model architecture, training procedure, evaluation protocol, and statistical analysis. Common omissions that undermine reproducibility.

9.3

Common Methodological Pitfalls in Medical AI Paperssoon

Data leakage, overfitting to the test set, inappropriate comparisons, missing external validation, cherry-picked metrics, and other errors that weaken or invalidate published results.

9.4

Sharing Code, Data, and Modelssoon

Best practices for open science in medical AI. GitHub repositories, model zoos, data sharing agreements, and balancing openness with patient privacy and commercial considerations.

9.5

Peer Review of AI Manuscripts: What Reviewers Look Forsoon

Understanding the evaluation criteria that journals and reviewers apply to AI papers. A practical guide for both authors preparing submissions and clinicians asked to review AI research.