Curriculum
12 Sections
44 Lessons
6 Weeks
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Module 1: Introduction to AI and ML in Healthcare
4
1.1
What is AI/ML?
1.2
The Importance of AI in Healthcare
1.3
Real-World Applications of AI in Healthcare (Diagnostics, Treatment, Research)
1.4
Challenges and Opportunities in AI for Healthcare
Module 2: Digitalization in Healthcare
4
2.1
Understanding Healthcare Data: EHRs, Imaging, and Genomics
2.2
Role of Technology in Healthcare Transformation
2.3
Telemedicine and Remote Patient Monitoring
2.4
Health Informatics and Decision Support Systems
Module 3: Basics of Python for Healthcare Professionals
4
3.1
Introduction to Python: Installing and Setting Up the Environment
3.2
Python Basics: Variables, Data Types, and Control Structures
3.3
Python Libraries for Healthcare Data Analysis (Pandas, NumPy)
3.4
Simple Data Analysis and Visualization
Module 4: Data Analytics in Healthcare
4
4.1
Introduction to Healthcare Data Analytics
4.2
Cleaning and Preparing Data for Analysis
4.3
Visualizing Healthcare Data with Basic Charts and Graphs
4.4
Case Study: Analyzing Patient Data for Trends
Module 5: Machine Learning Made Simple
4
5.1
What is Machine Learning? A Beginner’s Guide
5.2
Classification Basicsfor Disease Detection
5.3
Introduction to Regression Analysisfor Healthcare Outcomes
5.4
Case Study: Building a Simple Prediction Mode
Module 6: AI Applications in Healthcare
4
6.1
AI in Diagnostics: Automating Disease Detection
6.2
AI in Treatment Planning: Personalizing Patient Care
6.3
Predictive Analytics: Preventive Healthcare with AI
6.4
Case Study: Using AI to Improve Patient Care
Module 7: Introduction to Quantum Computing in Healthcare
4
7.1
What is Quantum Computing? A Non-Technical Introduction
7.2
How Quantum Computing Could Impact Healthcare
7.3
Realistic Examples and Current Progress in Quantum Healthcare
7.4
The Future of Quantum Computing in Medicine
Module 8: Ethics and Regulations in AI for Healthcare
4
8.1
Understanding AI Bias and Fairness
8.2
Ethical Concerns in AI Adoption
8.3
Regulations Governing AI in Healthcare (FDA, GDPR, HIPAA)
8.4
Case Study: Ethical Challenges in AI-Powered Healthcare
Module 9: DIY Projects and Case Studies
4
9.1
Project: Building a Simple Health Risk Prediction Tool
9.2
Case Study: AI in Imaging for Disease Diagnosis
9.3
Project: Creating a Basic Patient Feedback Analysis System
9.4
Case Study: AI-Powered Telemedicine
Module 10: Dissertation and Research Work
3
10.1
Guidelines for Choosing Topics
10.2
Suggested Areas: Digitization, AI in Diagnostics, Ethical AI
10.3
Dissertation Presentation and Submission
Module 11: Capstone Project
2
11.1
End-to-End Development of a Practical AI Application in Healthcare
11.2
Demonstration and Feedback Session
Module 12: Future Trends and Career Pathways in Healthcare AI
3
12.1
Emerging Tools and Technologies in Healthcare AI
12.2
Preparing for Future Roles in Healthcare Digitalization
12.3
Resources for Continuous Learning
PG Diploma in AI & Machine Learning for Healthcare Professionals
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