Workshop / Overview

Artificial intelligence is becoming increasingly critical to develop innovative, competitive and differentiated medical businesses and products. Unfortunately, over 80% of AI projects fail, due to lack of proper vision, guidance, and data strategy.
Deployed AI solutions always contain a degree of human and societal biases that may influence the results and have costly consequences for organisations.

In this workshop, delivered by five AI and data scientists with strong medical and enterprise backgrounds, you will learn how to address the main challenges in building successful AI projects for clinical research, from identifying problems where AI can readily add value, to getting the right team and resources for your projects.
You will learn how to leverage human-centric design to mitigate risks in AI projects in digital medicine.

We will provide you compelling and intuitive examples of when, why and how things can go wrong at different stages of an AI project and what can be done about it. In the second part of this workshop, you will learn how to use containerized Docker environments on RENKU, an open source tool for integration of the data science workflows.

Together we will implement a solution for a medical problem in jupyter notebooks using neural networks with TensorFlow and Keras. You will see in practice how to build model performance KPIs that reconcile model accuracy, business and fairness metrics.

Workshop / Outcome


  1. How to Innovate and differentiate through AI projects?
  2. What are the main challenges in AI projects?
  3. How to mitigate risks associated with AI?
  4. How to estimate the required resources (timeline, budget, team size)?
  5. Why do we need to address cognitive biases of users and how to do this?


  1. How to assess model impact on decisions?
  2. What is the right technology stack?
  3. How to build a reproducible and reliable workflow?
  4. What roles do I need for my AI project?


We offer 2h of free consulting with our team members on 3 selected, medical or bioinformatic related projects.

Workshop / Difficulty

Beginner level

Workshop / Prerequisites

Basic knowledge of Python and machine learning concepts. The participant must have his/her Laptop with internet access. 

Track / Co-organizers

Oksana Riba Grognuz

Senior Data Science Engineer, Swiss Data Science Center (ETH Zürich/EPFL)

Pawel Rosikiewicz

Team Leader,

Onur Yürüten

Data Science Manager, Visto Consulting SA & Member, SwissAI

AMLD EPFL 2021 / Workshops

Towards ethical AI – practical tools for responsible data scientists

With Johan Rochel & Lea Strohm

10:00-11:30 November 10Online

How to make your NLP system multilingual

With Adam Bittlingmayer & Nerses Nersesyan

10:00-12:00 March 02Online

Deep Learning-Driven Text Summarization & Explainability with Reuters News Data

With Nadja Herger, Nina Hristozova & Andreea Iuga

15:00-17:30 March 02Online

AMLD / Global partners