Utrecht Circle by AI4ALL: AI in Healthcare

AI in Healthcare- The Power of Data and the Challenge of Bias

Join us in Utrecht for an exciting event exploring how AI is transforming healthcare. We’ll look at the opportunities and challenges of using AI in healthcare, bringing practical insights and academic expertise to the conversation. Data scientist Laura Veerhoek (UMC Utrecht) will share how AI can enhance hospital workflows and speed up healthcare delivery, while PhD candidate Maria Galanty (UvA) will provide an academic perspective on the use of AI in healthcare, addressing the important issue of bias in medical documentation and its implications. The session will conclude with an open discussion, so come ready with your questions!

AI in Healthcare: The Power of Data and the Challenge of Bias

AI is transforming healthcare, from diagnostics, personalised treatments and robotic-assisted surgery. Yet AI is only as reliable as the data it learns from - and in medicine, data is often difficult to obtain and, when available, frequently incomplete or unbalanced. A further challenge lies in medical annotations, which are costly and time-consuming because they require clinical expertise. These limitations can create disparities in model performance. In this talk, Maria Galanty will explore how AI is being applied in healthcare, why data quality and expert annotation are so important, and how dataset documentation can help discover medical bias.

Meet our speaker:

Maria Galanty holds a Bachelor’s degree in Mathematics and Cognitive Science from the University of Warsaw and a Master’s degree (MSc) in Artificial Intelligence from Utrecht University.

She is currently a PhD candidate at the qurAI group (University of Amsterdam, Amsterdam UMC), within the Intensive Care Lab, under the supervision of Clarisa Sánchez. Her doctoral research is part of the UvA Research Priority Agenda AI for Health Decision-Making, a cross-disciplinary collaboration spanning the Faculties of Medicine, Science, Humanities, and Law.

Maria’s research interests center on bias in medical AI and data annotation practices, with a particular focus on ensuring robustness in AI solutions. She also works on AI applications in intensive care units, especially the use of ECG signals for the detection and prediction of atrial fibrillation.

✨The session will conclude with an open discussion, so come with your questions.

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