- Level: Beginner
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to AI in Healthcare
Learning Outcome: Understand AI applications in medical diagnostics.1.1 Overview of healthcare challenges.
1.2 AI applications in diagnostics and patient care.
1.3 Ethical considerations in AI healthcare.
Practical Component
Analyze sample patient datasets.
Week 2: Medical Data Preparation and Analysis
Learning Outcome:Process and analyze medical data for AI systems.1.4 Sources and types of medical data (imaging, text, sensors).
1.5 Data preprocessing techniques for healthcare.
1.6 Identifying key features for diagnostic models.
Practical Component
Prepare medical imaging data for analysis.
Week 3: Building AI Models for Diagnostics
Learning Outcome: Develop AI models to assist in diagnostics.1.7 Image classification for disease detection.
1.8 Natural Language Processing (NLP) for medical records.
1.9 Testing and validating diagnostic models.
Practical Component
Build an AI model to classify X-ray images.
Week 4: Advanced Diagnostic Systems
Learning Outcome: Explore advanced techniques for better diagnostics.1.10 Deep learning for medical imaging.
1.11 Predictive models for patient outcomes.
1.12 Real-time diagnostic systems.
Practical Component
Develop a deep learning model for MRI analysis.
Week 5: Challenges and Ethics in AI Healthcare
Learning Outcome: Understand limitations and ethical concerns.1.13 Challenges in implementing AI in healthcare.
1.14 Addressing biases in medical datasets.
1.15 Ensuring data privacy and security.
Practical Component
Analyze case studies of AI healthcare failures.
Week 6: Final Project
Learning Outcome: Build and present an AI diagnostic solution.1.16 Define a use case (e.g., cancer detection).
1.17 Develop and test the solution.
1.18 Present the results to peers and instructors.
Practical Component
Demonstrate your diagnostic system for a real-world problem.