- Level: Intermediate
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to AI in Healthcare
Learning Outcome: Understand how AI is transforming healthcare.1.1 Overview of healthcare challenges AI can address.
1.2 AI applications in diagnosis and treatment.
1.3 Real-world examples of AI in healthcare.
Practical Component
Analyze healthcare datasets for basic insights.
Week 2: AI for Medical Imaging and Diagnosis
Learning Outcome: Learn how AI supports imaging and diagnosis.1.4 Image classification and anomaly detection.
1.5 AI for early disease detection (e.g., cancer screening).
1.6 Tools and platforms for medical imaging AI.
Practical Component
Build an AI model for basic X-ray image analysis.
Week 3: AI in Personalized Medicine
Learning Outcome: Explore AI's role in personalized treatments.1.7 Predicting patient outcomes using AI.
1.8 Drug discovery and design with AI.
1.9 Creating personalized care plans.
Practical Component
Use AI to predict treatment responses.
Week 4: Patient Monitoring and AI
Learning Outcome: Use AI to improve patient monitoring.1.10 Wearable devices and real-time health tracking.
1.11 AI for remote patient monitoring.
1.12 Predicting medical emergencies using AI.
Practical Component
Develop an AI model to analyze wearable device data.
Week 5: Ethical and Regulatory Considerations
Learning Outcome: Implement AI responsibly in healthcare.1.13 Addressing data privacy and security.
1.14 AI bias in healthcare and its impact.
1.15 Navigating regulatory frameworks.
Practical Component
Evaluate an AI healthcare solution for ethical compliance.
Week 6: Final Project
Learning Outcome: Build a complete AI healthcare solution.1.16 Define a healthcare challenge (e.g., diagnosis prediction).
1.17 Develop and test the solution.
1.18 Present the project to peers or experts.
Practical Component
Deliver a working AI healthcare model.