1 Introduction to AI in Medical Field

• Overview of Artificial Intelligence, ML, DL
• Traditional vs Medical AI
• Diagnosis, Clinical Support

2 Python

• Python Basics
• Variables and Simple Data Types
• Lists
• If Statements
• Dictionary
• User Input
• Loops
• Functions
• Classes
• Files and Exceptions

3 Python Practice & Mini Projects

• 2–4 Projects

4 Data Visualization & Preprocessing

• Data cleaning & normalization
• Feature Engineering
• Medical datasets training
• NumPy
• Seaborn
• Pandas
• Matplotlib

5 Machine Learning for Diagnosis

• Classification Models (Logistic Regression, Random Forest, XGBoost)
• Model Evaluation Metrics in Medical Context
• //Sensitivity vs Specificity, Precision-Recal

6 Deep Learning

• Convolutional Neural Networks (CNNs)
• Transfer Learning using Pretrained Models (ResNet, VGG)

7 NLP in Healthcare

• OCR for Prescriptions
• Scanned Documents
• Medical Report Summarization
• Named Entity Recognition (Symptoms, Drugs, Diagnosis Extraction)
• Rule-Based Systems + AI Hybrid Models
• Drug Recommendation Engines
• Evidence-Based Treatment Suggestion

8 AI Deployment

• Deploy on cloud & apps

9Business Management

• Sales, Marketing , in Healthcare & Software
• Digital Marketing, SEO, Social Media Strategies

10 LinkedIn & CV Writing -Free Lance

• Freelance in software and healthcare medical

11 Note:

• Math (Linear Algebra, Probability & Statistics) is integrated across all modules.

11 Our Diploma instractors

• ENG-Ranim Ashraf
• ENG-Mohame Hassan