Explainable AI for Life Science

In this workshop, we will give an introduction to explainable AI (xAI) and its role in the life sciences. Through interactive lectures and hands-on notebooks, participants will apply explanation methods such as SHAP, feature importance, saliency maps, and GradCAM to real medical datasets – including brain stroke prediction from tabular data and classification of chest X-ray images. By the end of the workshop, you will have hands-on experience in generating, interpreting, and critically validating AI explanations on real-life science problems

Prerequisites

  • Basic familiarity with Python is expected.

  • Experience with machine learning is preferable, but no prior experience with explainable AI is required.

Pre-workshop setup

Lesson

Reference

Learning outcomes

By the end of this module, learners should:

  • Understand use cases of AI in life sciences and how explainable AI (xAI) helps to make a step towards trustworthy AI in the life sciences.

  • Describe the main categories of xAI methods and explain the differences between them.

  • Evaluate model explanations critically, recognising that xAI is a tool – not a guarantee – and that explanations must be validated by domain experts.

See also

Credit

Don’t forget to check out additional course materials from NBIS Sweden [MAK+26], and AI:AT [Wac26].

License

CC BY-SA for media and pedagogical material

Copyright © 2026, Sweden AI Factory, Ivan Cucchi, Fahria Kabir, Ashwin Mohanan, Nishat Mowla, Ana Tanevska, Erik Ylipää. . This material is released by Sweden AI Factory under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

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MIT License

Copyright (c) 2026, Sweden AI Factory, Ivan Cucchi, Fahria Kabir, Ashwin Mohanan, Nishat Mowla, Ana Tanevska, Erik Ylipää.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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[MAK+26]

Claudio Mirabello, Christophe Avenel, Marcin Kierczak, Per Unneberg, and Erik Ylipää. NBIS Workshop on Neural Networks and Deep Learning. 2026. URL: https://nbisweden.github.io/workshop_NN_DL/ (visited on 2026-10-21).

[Wac26]

Anahid Wachsenegger. Hands-on Explainable AI (XAI): From Data Understanding to Model Interpretation. April 2026. original-date: 2026-02-23T09:11:10Z. URL: https://ai-at.eu/training/hands-on-explainable-ai-xai-from-data-understanding-to-model-interpretation-2 (visited on 2026-10-21).