Understand, model, and apply artificial intelligence in medical and healthcare environments
This training course explores the practical applications of artificial intelligence in the field of healthcare.
Is it for you ?
Healthcare professionals
Prerequisites
• Basic knowledge of Python and machine learning
• Basic understanding of data processing
What You'll Walk Away With
- ✓ Understand the specificities of AI in healthcare and select appropriate approaches for medical data
- ✓ Identify and analyze real-world use cases (diagnosis, risk prediction, hospital optimization)
- ✓ Explore, clean, and visualize medical datasets to extract actionable insights
- ✓ Build and evaluate predictive healthcare models using tools such as scikit-learn or Keras
- ✓ Integrate ethical, regulatory, and adoption considerations into AI healthcare projects
Training content
1 Introduction to AI in the medical sector
- Key definitions and concepts
- Differences between AI, machine learning, and deep learning
- Specific characteristics of medical data
2 Use cases of AI in healthcare
- AI-assisted diagnosis (radiology, dermatology, pathology)
- Risk prediction and preventive medicine
- Hospital flow management and resource optimization
- Personalized medicine and genomics
3 Workshop 1: Exploring a medical dataset
- Data cleaning and visualization (e.g., patient data, imaging)
- Exploratory analysis with Python (Pandas, Matplotlib)
4 Predictive modeling in healthcare
- Logistic regression, decision trees, neural networks
- Performance evaluation: precision, recall, ROC curve
- Anomaly detection and disease classification
5 Ethical and regulatory issues
- Health data protection (Law 25, HDS)
- Algorithmic bias and fairness
- Acceptability by professionals and patients
6 Prototyping a healthcare AI application
- Choosing a use case (e.g., diabetes prediction, file sorting)
- Building a simple model with scikit-learn or Keras
- Presentation of results and discussion
7 Workshop 2: Mini-project – Creating a predictive model
- Training on a medical dataset
- Interpreting results
- Reflection on integration into a care pathway
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📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
