AI and ML Healthcare Training Course
Master Artificial Intelligence and Machine Learning for healthcare—from medical imaging analysis to clinical prediction models. Learn to build diagnostic tools, process EHR data, and deploy AI solutions that improve patient outcomes while meeting Dubai Health Authority standards.
Course Duration: 6 Months
Delivery Mode:Classroom / Online / Blended
AI and ML Healthcare Training – Program Overview
The AI and ML in Healthcare Training Program is a 6-month (24–26-week) comprehensive course that builds both foundational and advanced understanding of artificial intelligence and machine learning applications in healthcare. Learners explore medical data analysis, predictive modeling, and diagnostic automation through guided labs, simulations, and real-world clinical projects.
The program covers critical areas including Python programming, supervised and unsupervised learning, deep learning for medical imaging, natural language processing (NLP), and AI deployment with cloud integration. With a focus on ethical AI practices, healthcare data governance, and UAE-specific healthcare needs, participants gain the technical expertise and applied insight needed to implement AI-driven innovations across hospitals, research centers, and healthcare organizations.
AI & ML Healthcare Training in Dubai - Who is it for?
- Healthcare professionals
- Data analysts
- Students interested in exploring AI and ML in healthcare
- Medical researchers
- IT specialists
Skills You’ll Develop with Our AI and ML Healthcare Training in Dubai
Gain future-ready technical and analytical skills that empower you to apply artificial intelligence in healthcare course Dubai across the healthcare settings.
Healthcare Data Analysis
Learn to collect, clean, and interpret complex medical datasets to generate actionable insights for better patient outcomes.
Build and train ML models for disease prediction, diagnostics, and personalized treatment planning.
Deep Learning & Medical Imaging
Gain hands-on experience using neural networks to analyze X-rays, MRIs, and CT scans for faster, more accurate diagnosis.
Natural Language Processing (NLP)
Apply AI techniques to extract meaningful insights from electronic health records and clinical documentation.
Software, Tools, Languages & Frameworks
AI & ML Healthcare Training - Course Curriculum
Module 1: Introduction to AI in Healthcare (6 hrs)
- Overview of AI in healthcare: evolution, applications, and relevance to UAE Vision 2031
- Key AI technologies: Machine Learning, NLP, Computer Vision, Robotics, GENAI
- Healthcare transformation through AI: Smart hospitals, digital health records
- Ethical and legal aspects: Dubai Health Authority (DHA) regulations, HIPAA, patient consent
- Use cases in UAE hospitals: radiology automation, virtual nursing assistants, AI triage systems
Module 2: Python for AI (18 hrs)
- Python Basics: Syntax, variables, operators, conditionals, loops, functions
- Data Structures: Lists, dictionaries, tuples, sets relevant to medical data organization
- OOP Concepts: Encapsulation, inheritance, creating reusable health analysis modules
- NumPy: Medical imaging arrays and sensor data
- Pandas: Cleaning and analyzing hospital, pharmacy, and EHR data
- Matplotlib/Seaborn: Plotting lab values, vital trends, and seasonal patterns in illnesses
Module 3: Healthcare Data Preparation and Visualization (16 hrs)
- Data Sources: EHRs, insurance records, public datasets (MIMIC, WHO), UAE health portals
- Data Cleaning: Null values, outlier detection in vitals and dosage info, duplicates
- Feature Engineering: Symptoms, medication durations, lab result ranges, dummy variables
- Data Visualization: Dashboards for disease trends, mortality rates, health metrics
- Exploratory Data Analysis (EDA): Age vs. condition analysis, gender distribution in diagnoses
- Hand on using Cancer dataset, Diabetes dataset
Module 4: Supervised Machine Learning for Healthcare (20 hrs)
- What is Supervised learning
- Regression vs Classification
- Overview of supervised learning in diagnostics and predictions
- Linear regression on Length of Stay in Hospital dataset
- Logistic Regression: Heart disease risk, diabetes classification
- KNN, Decision Trees, Random Forests: Early stage cancer detection, patient triage automation
- Model Metrics: R2 Score, Mean Squared Error, Precision, Recall, F1 Score, especially for imbalanced datasets
- Healthcare Use Cases: Readmission prediction, treatment recommendation systems
Module 5: Unsupervised Machine Learning in Healthcare (12 hrs)
- Clustering patients by symptoms or disease severity
- Dimensionality reduction: PCA on genetic data and wearable sensors
- Algorithms: K-Means, Hierarchical Clustering,PCA
- Use Cases: Grouping patient profiles, anomaly detection in medical billing
Module 6: Deep Learning and Neural Networks in Medical Applications (20 hrs)
- Concept of neural network – Perceptron, Neuron
- Types of Neural network – ANN, CNN, RNN and their differences
- ANN: Basic concepts, structure, forward/backward propagation in health scenarios
- Apply ANN to Stroke prediction dataset (Keras / TensorFlow)
- CNN: Image classification (chest X-rays, CT scans) with Keras/TensorFlow
- RNN/LSTM: ICU patient time series, ECG prediction
- Transfer Learning using pre-trained models (e.g., ResNet, VGG for MRI data)
- Hands-on: Build and test a CNN model for diabetic retinopathy
Module 7: Natural Language Processing (NLP) in Healthcare (16 hrs)
- Text preprocessing of clinical notes, patient histories
- NER: Extraction of medical conditions, medications, procedures
- Sentiment analysis of patient feedback and chatbot dialogues
- Medical text classification: Categorizing reports and claims
- Use of pretrained medical BERT models for summarizing research
Module 8: AI Project Deployment & Cloud Integration in Healthcare (8 hrs)
- Deploying predictive tools via Flask/FastAPI as APIs
- Containerization: Docker for healthcare model portability
- Intro to cloud (AWS): Using Dubai-based cloud hosting services
- Streamlit: Real-time dashboards for clinics and hospitals
- Security: Encrypting medical data, managing API access
Module 9: Capstone Project – Healthcare AI Solution (16 hrs)
- Students select a real-world healthcare use case relevant to Dubai (e.g., emergency readmission, remote patient monitoring)
- Build E2E solution: Data processing, model training, evaluation, UI
- Incorporate healthcare ethics and DHA compliance in documentation
- Project Deliverables: Deployed solution, code, model, documentation, pitch presentation
- Sample Projects
- Predicting Hospital Readmission in Diabetic Patients
- Lung Disease Classification from Chest X-ray Images (Using CNN)
- ECG Signal Classification for Arrhythmia Detection (Using RNN/LSTM)
- Arabic-English Medical Chatbot for Patient FAQs
- Early Detection of Heat-Related Illness During Dubai Summers
