This practical course teaches medical researchers to apply AI to their work. You will learn to identify research problems where deep learning excels at extracting structure from unstructured data, such as patient images, raw clinical notes, or wearable sensor data. You will gain hands-on skills to apply or fine-tune powerful, pre-trained models (like CNNs and Transformers) with clinical or epidemiological case studies, with a focus on interpretation and validation. This includes using explainability techniques to understand why a model makes a prediction, identifying domain shift to see if a model will work on a new population, and testing for data biases that can perpetuate health disparities. You will leave with the ability to build/apply, interpret, and critically validate AI models for medical research.

The course alternates between lectures and practical exercises.

The course consists of 3 days.

Topics of Day 1:Foundations & Translation

• The “Why” & “What” – Study Design & Conceptual Translation

Topics day 2: Unlocking Unstructured Data

• “How?” – What data and What methods ?

Topics day 3: Validation, Bias and Critical Appraisal :

• For Whom it works? How to improve?

1. Identify research problems suitable for deep learning versus traditional machine learning.
2. Fine-tune pre-trained deep learning models for tasks using health data.
3. Identify the input features that drive a model’s prediction using explainability techniques.
4. Identify domain shift and bias to determine if a model can be applied to a new population.

Target audience

Medical and epidemiological researchers interested in using AI as a tool for their research
or
who are interested in the most common applications of AI in epidemiological and medical research

Course prerequisites

The following concepts are assumed known by participants at the start of this course:
basics of python would be an advantage for practicals but not strictly necessary

Course material

The course materials (lectures, assignments, feedback of the assignments etc) are available on Canvas, our digital learning environment. The documents will remain available on Canvas for at least one year.

Literature

Recommended reading

There is no exam for this course

A certificate of participation will be granted to all students who have attended at least 80% of the classes. The number of contact hours will be stated on the certificate.

Only for Dutch medical specialists!

On the final day of the course, you need to sign the attendance list if you wish to obtain KNMG accreditation credits (PE-points).

To qualify for these credits, there is an attendance requirement of 100%.