AI & Health Winter School: abstracts
Tuesday 12th March
Leveraging blood derived omics for AI enhanced cancer detection
The Next Frontier: Medical Foundation Models
Reinforcement Learning: Applications to Healthcare Solutions
Wednesday 13th March
Emma Beauxis-Aussalet (VU, Computer Science, User Centric Data Science group)
Towards responsible and explainable AI
AI can have critical impacts on people or society, for instance by creating bias and discrimination. These impacts are difficult to assess and communicate to practitioners or end-users, and this gap worsens the ethical issues. This lecture will discuss the means to inform AI practitioners and end-users about the risks of error and bias. First, we will investigate this issue through the lens of a seemingly simple task: measuring AI errors. We will highlight key challenges with collecting reliable test data, with ensuring that vulnerable populations are well-represented, and with assessing the test data as well as the test results. Then we will overview the ways to explain AI results, and to understand why certain results are obtained. We will outline the state-of-the-art, and draw critical views on its limitations. Finally, we will argue that designing responsible AI systems also requires designing the human organisations in which such AI systems take place.
AI for daily-life stress research
Automated recognition of (recovery in) functioning of COVID-19 patients with natural language processing
Thursday 14th March
End-user adoption of AI in clinical practice: A socio-technical perspective
Knowledge Engineering for Hybrid Intelligence
What no eye has seen: how AI is revolutionizing medical imaging
Friday 15th March
Computational Methods in Drug Discovery: AI and Physics Based Approaches