Medical Artificial Intelligence Course

Lesson 1 • Types of Medical AI Tasks

Surveys classification, regression, segmentation, and generation tasks in medicine. Helps learners match task types to appropriate algorithms.

Lesson 2 • What Is Medical AI

Defines AI, machine learning, and deep learning in a clinical context. Establishes shared vocabulary used throughout the course.

Lesson 3 • Key Stakeholders and Roles

Maps the ecosystem of clinicians, data scientists, regulators, and patients. Clarifies collaboration responsibilities for successful AI deployment.

Lesson 4 • Clinical Problem Framing

Teaches how to translate a clinical challenge into an AI problem statement. Connects problem framing to downstream model design choices.

Lesson 1 • Data Quality Assessment

Identifies completeness, accuracy, consistency, and timeliness as quality dimensions. Prepares learners to audit datasets before model training.

Lesson 2 • Privacy, Security, and Governance

Covers patient data protection principles and institutional governance frameworks. Grounds learners in responsible data stewardship practices.

Lesson 3 • Data Standards and Interoperability

Explains clinical coding systems and health data exchange standards. Enables learners to work with structured clinical datasets effectively.

Lesson 4 • Clinical Data Sources and Types

Covers EHR records, imaging archives, genomics, and wearable streams. Establishes the breadth of data available for medical AI projects.

Lesson 1 • Model Training and Optimization

Covers loss functions, gradient descent, hyperparameter tuning, and cross-validation. Equips learners to train robust models on clinical datasets.

Lesson 2 • Unsupervised Learning in Medicine

Introduces clustering, dimensionality reduction, and anomaly detection for clinical discovery. Enables learners to find patterns without labeled outcomes.

Lesson 3 • Evaluating Clinical ML Models

Teaches AUC-ROC, precision-recall, calibration, and net benefit analysis. Connects statistical metrics to clinically meaningful performance thresholds.

Lesson 4 • Supervised Learning Essentials

Explains logistic regression, decision trees, and support vector machines in clinical settings. Connects algorithm choice to interpretability and performance needs.

Lesson 1 • Medical Image Segmentation

Covers encoder-decoder networks and attention mechanisms for organ and lesion delineation. Prepares learners to build segmentation pipelines for clinical workflows.

Lesson 2 • Imaging AI Across Specialties

Surveys deployed imaging AI in radiology, pathology, dermatology, and ophthalmology. Illustrates how architecture choices vary by imaging modality.

Lesson 3 • Vision Transformers in Medicine

Introduces self-attention and patch-based image processing for medical tasks. Compares transformer performance against CNNs on clinical imaging benchmarks.

Lesson 4 • Convolutional Neural Networks

Details convolution, pooling, and feature map hierarchies for image analysis. Connects CNN design choices to radiology and pathology performance.

Lesson 1 • Evaluating Clinical NLP Systems

Covers F1 score, exact match, BLEU, and clinical expert evaluation methods. Connects NLP metrics to real-world clinical utility assessments.

Lesson 2 • Transformer Models for Clinical NLP

Explains BERT-style pretraining and fine-tuning on clinical corpora. Connects transformer architecture to state-of-the-art clinical NLP performance.

Lesson 3 • Clinical Text and NLP Basics

Covers tokenization, named entity recognition, and clinical note structure. Establishes NLP fundamentals before addressing healthcare-specific challenges.

Lesson 4 • Information Extraction from EHRs

Teaches relation extraction, temporal reasoning, and phenotyping from clinical notes. Enables learners to convert free text into structured AI-ready features.

Lesson 1 • Conducting a Fairness Audit

Provides a structured workflow for auditing model performance across demographic subgroups. Prepares learners to document and communicate audit findings.

Lesson 2 • Fairness Metrics and Definitions

Explains demographic parity, equalized odds, and calibration across subgroups. Clarifies trade-offs between competing fairness criteria in clinical contexts.

Lesson 3 • Ethical Frameworks for Medical AI

Applies beneficence, non-maleficence, autonomy, and justice to AI deployment decisions. Connects bioethical principles to practical AI governance choices.

Lesson 4 • Bias Mitigation Strategies

Covers pre-processing, in-processing, and post-processing debiasing techniques. Equips learners to select mitigation methods appropriate to clinical constraints.

Lesson 1 • Dataset and Model Documentation

Covers model cards, datasheets, and intended-use statements for regulatory submissions. Teaches learners to produce documentation that satisfies reviewers.

Lesson 2 • Continuous Monitoring and Revalidation

Addresses model drift detection, performance dashboards, and retraining triggers. Connects ongoing monitoring to patient safety and regulatory compliance.

Lesson 3 • Evidence Standards for Clinical AI

Compares retrospective, prospective, and randomized trial designs for AI validation. Establishes the evidence hierarchy expected by regulators and clinicians.

Lesson 4 • Regulatory Frameworks for AI Devices

Explains software-as-a-medical-device classification, risk tiers, and approval pathways. Prepares learners to navigate regulatory requirements without jurisdiction-specific detail.

Lesson 1 • Workflow Integration Design

Maps AI outputs to clinical decision points and EHR alert systems. Ensures AI recommendations reach clinicians at the right moment in care delivery.

Lesson 2 • Change Management and Adoption

Applies change management principles to clinical AI rollouts and resistance mitigation. Connects staff engagement strategies to sustained tool adoption.

Lesson 3 • Measuring Clinical and Operational Impact

Defines outcome metrics spanning clinical quality, efficiency, and patient experience. Equips learners to build business cases and report AI value.

Lesson 4 • Scaling and Sustaining AI Programs

Covers governance structures, infrastructure scaling, and portfolio management for AI programs. Prepares learners to grow from pilot to enterprise-wide deployment.