Speaker
Description
Artificial intelligence (AI) is increasingly being used in various disciplines. Examples of this include medical image processing, complex prediction and decision support models and thereby integrating with the field of biostatistics. In this context, integrating AI and machine learning (ML) methods within courses of biostatistics taught to students of medicine, health and life sciences offers a unique opportunity to highlight the relevance of statistics in modern AI applications.
This talk discusses integrating AI topics into biostatistics education. We argue that statistics is fundamental to AI development, ensuring methodological rigor, robustness, fairness, and interpretability while quantifying uncertainty. Without statistics, AI risks being reduced to "black box" methods. Beyond that, key topics concerning the role of biostatistics in AI applications include the planning of studies and study designs, evaluating data quality, managing missing data and outliers, and critical interpretation of the outcome distinguishing causality from correlation.
We emphasize the need for targeted educational initiatives that blend biostatistical and AI expertise enhancing AI-related statistical literacy in curricula. This is essential for training future academics to recognize the role of biostatistics in AI methods and to support the responsible and safe application of AI.
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