AI in Medicine Course

Lesson 1 • History of AI in Healthcare

Traces AI milestones from early expert systems to modern neural networks in medicine. Provides context for understanding current capabilities and limitations.

Lesson 2 • What Is Artificial Intelligence

Defines AI, machine learning, and deep learning with medical examples. Grounds subsequent chapters by establishing shared vocabulary.

Lesson 3 • Key AI Techniques Overview

Introduces supervised, unsupervised, and reinforcement learning at a conceptual level. Sets the stage for deeper technical coverage in later chapters.

Lesson 4 • Types of Medical Data

Surveys structured, unstructured, and multimodal data generated in clinical settings. Prepares learners to match data types to appropriate AI methods.

Lesson 1 • Data Preprocessing Techniques

Applies normalisation, encoding, and imputation to raw clinical datasets. Produces analysis-ready inputs required by the modelling chapters ahead.

Lesson 2 • Data Privacy and Security

Addresses de-identification, encryption, and access control for sensitive health data. Ensures compliance with patient privacy principles central to all AI projects.

Lesson 3 • Data Quality and Validation

Identifies common quality issues such as missing values, duplicates, and label noise. Teaches validation strategies that prevent downstream model errors.

Lesson 4 • Feature Engineering for Clinical Data

Transforms raw variables into informative features that improve model performance. Bridges data preparation and the machine learning methods introduced next.

Lesson 1 • Model Evaluation and Validation

Measures model performance using clinical metrics and cross-validation strategies. Ensures models meet accuracy and reliability standards before deployment.

Lesson 2 • Unsupervised Learning in Patient Stratification

Applies clustering and dimensionality reduction to discover patient subgroups. Enables phenotyping tasks where labelled data is unavailable.

Lesson 3 • Handling Imbalanced Clinical Datasets

Addresses class imbalance common in rare-disease and adverse-event datasets. Applies resampling and cost-sensitive methods to improve minority-class detection.

Lesson 4 • Regression and Prognosis Modelling

Uses regression techniques to predict continuous outcomes such as length of stay. Extends classification skills to quantitative clinical prediction.

Lesson 1 • Neural Network Fundamentals

Explains neurons, layers, activation functions, and backpropagation in accessible terms. Provides the theoretical base for all deep learning sections that follow.

Lesson 2 • Imaging AI Performance and Limitations

Evaluates imaging models using radiological metrics and identifies failure modes. Prepares learners to critically assess AI tools before clinical adoption.

Lesson 3 • Transfer Learning and Pretrained Models

Adapts models pretrained on large datasets to small medical imaging collections. Reduces training time and data requirements for clinical AI projects.

Lesson 4 • Pathology and Whole-Slide Image Analysis

Applies deep learning to gigapixel pathology slides for cancer grading and detection. Extends imaging skills to the distinct challenges of digital pathology.

Lesson 1 • Text Preprocessing and Representation

Converts raw clinical text into numerical representations suitable for modelling. Bridges raw data to the NLP algorithms introduced in subsequent sections.

Lesson 2 • Clinical Text Characteristics

Describes the unique features of medical language including abbreviations and negation. Motivates specialised NLP approaches used throughout this chapter.

Lesson 3 • Named Entity Recognition and Extraction

Identifies clinical entities such as diagnoses, medications, and procedures in text. Enables structured data extraction from unstructured clinical documentation.

Lesson 4 • Large Language Models in Healthcare

Applies transformer-based LLMs to clinical summarization, coding, and Q&A tasks. Addresses the most current and impactful NLP paradigm in medicine.

Lesson 1 • Bias Mitigation Strategies

Implements pre-processing, in-processing, and post-processing debiasing techniques. Reduces performance gaps across demographic groups without sacrificing overall accuracy.

Lesson 2 • Bias Detection and Auditing

Applies statistical tests and visualization tools to detect disparate model performance. Produces audit reports that support regulatory and institutional review.

Lesson 3 • Ethical Frameworks for Medical AI

Applies beneficence, autonomy, justice, and non-maleficence to AI design decisions. Connects technical fairness work to broader professional and institutional ethics.

Lesson 4 • Sources of Bias in Medical AI

Traces bias origins from data collection through model deployment in clinical settings. Establishes why fairness analysis is mandatory before any AI system goes live.

Lesson 1 • Post-Market Monitoring of AI Systems

Designs continuous monitoring plans to detect performance drift after deployment. Closes the validation loop by linking real-world outcomes back to model updates.

Lesson 2 • Designing AI Clinical Validation Studies

Covers endpoint selection, sample size, and comparator choice for AI trials. Ensures studies generate evidence that satisfies both scientific and regulatory standards.

Lesson 3 • Reporting Standards and Transparency

Applies established AI reporting guidelines to manuscripts and regulatory documents. Promotes reproducibility and trust in published clinical AI research.

Lesson 4 • Levels of Clinical Evidence for AI

Maps AI validation evidence from retrospective studies to randomized trials. Establishes the evidentiary hierarchy that regulators and clinicians expect.

Lesson 1 • AI Governance and Oversight Structures

Establishes committees, policies, and escalation paths for ongoing AI oversight. Creates the institutional infrastructure needed to sustain responsible AI use.

Lesson 2 • Measuring Clinical and Economic Impact

Evaluates AI impact using clinical outcome metrics, cost analyses, and ROI frameworks. Demonstrates value to leadership and justifies continued investment in AI programmes.

Lesson 3 • Clinical Workflow Analysis and Redesign

Maps existing workflows to identify integration points and redesign processes around AI. Prevents the common failure of deploying AI without adapting surrounding clinical steps.

Lesson 4 • Change Management and Clinician Adoption

Applies change management models to overcome resistance and build clinician trust in AI. Addresses the human factors that most often determine implementation success or failure.