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

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Description:

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 speakers:

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.

Welmoed Tjepkema is a data scientist at the AI for Health team of the University Medical Centre of Utrecht with a background in biomedical engineering. During her master’s thesis, she worked on using AI models to detect patient deterioration based on wearable vital sign data collected at the hospital. After that, she joined the AI for Health team to keep exploring how AI can be used in healthcare, including projects like building a model to predict patient no-shows.

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

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