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.

This workshop is delivered in Applied Machine Learning Days (AMLD-2021). Click here to access to the workshop page.