Dr. Nils Daniel Forkert, PhD (h-index 44, 7661 citations) is a professor at the University of Calgary in the Departments of Radiology, Clinical Neuroscience, and Electrical and Software Engineering.
He received his German Diploma in Computer Science from the University of Hamburg in 2009, his Master's degree in Medical Physics from the Technical University of Kaiserslautern in 2012 and his PhD in Computer Science from the University of Hamburg in 2013. He then completed a postdoctoral fellowship at Stanford University before joining the University of Calgary as an Assistant Professor in 2014.
Dr. Forkert is an imaging and machine learning scientist. He develops new image processing methods, predictive algorithms and software tools for analyzing medical data. This includes the extraction of clinically relevant parameters and biomarkers from data describing the morphology and function of organs - with the aim of supporting clinical studies and preclinical research as well as developing computer-aided diagnostic systems and patient-specific, precision medicine prediction models based on multimodal medical data using machine learning.
Dr. Forkert holds a Canada Research Chair (Tier 2) in Medical Image Analysis, is Director of the Child Health Data Science Program at the Alberta Children's Hospital Research Institute, and is Theme Leader for Machine Learning in Neuroscience at the Hotchkiss Brain Institute at the University of Calgary.
He has published over 220 peer-reviewed articles, more than 90 full conference papers, one book and two book chapters. He has also secured major funding as principal or co-principal investigator from the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council, the Heart and Stroke Foundation, the Calgary Foundation, and the National Institutes of Health.
Artificial intelligence (AI) has already become a vital tool for transforming the vast amounts of data acquired and available into tangible benefits in our increasingly data-driven world. Healthcare is no exception to this — the advancements in diagnostic technologies have significantly increased the volume and complexity of medical data collected, both at the individual patient level and across populations. Among these data sources, medical imaging stands out as the largest and most information-rich, yet also one of the most challenging to analyze. The sheer volume and complexity of imaging data, especially when integrated with other clinical information, can overwhelm clinicians and make timely, accurate interpretation difficult. In this context, the AI domain, in particular machine learning (ML), offers powerful methods to support clinical decision-making, reduce cognitive burden, and enable healthcare professionals to focus more on complex cases and patient interactions, ultimately promoting precision and equity in care delivery.
Despite this promise and potential, the clinical adoption of ML models in radiology remains rather limited. One of the major adoption barriers is related to the growing body of evidence showing that ML models used for medical image analysis can exhibit biased or discriminatory behavior, particularly when used for computer-aided diagnosis. While these concerns are increasingly recognized, there is still a lack of systematic research into how biases embedded in medical images influence ML model structure and behavior, and how diagnostic systems can be designed to be fairer and more equitable. Importantly and often overlooked, addressing these challenges requires more than technical expertise as machine learning is not a value-neutral technology. It is deeply embedded in social contexts and shaped by the data it is trained with, the systems it operates within, and the societal values it reflects.
This presentation will highlight our recent work aimed at identifying and understanding biases in medical imaging data and ML models. More precisely, it will introduce a novel synthetic data simulation framework that enables controlled, systematic evaluation of how specific imaging biases affect model performance, and how mitigation strategies can reduce disparities between subgroups. Building on this, it will be demonstrated how convolutional neural networks encode different types of biases across their layers and how these encodings contribute to shortcut learning. Moreover, it will be shown how advanced AI methods can be used to uncover the underlying mechanisms of unfair model behavior. Finally, the presentation will take a first step toward connecting current technical definitions of AI fairness with their broader sociotechnical impacts, emphasizing the need for interdisciplinary approaches to fairness in medical AI. Together, this presentation underscores the importance of controlled experimentation, explainability, and sociotechnical awareness in developing AI systems that are not only accurate but also equitable and trustworthy in clinical practice.
Antonio Krüger is CEO and scientific director of the German Research Center for Artificial Intelligence GmbH (DFKI) and head of the department “Cognitive Assistants” at DFKI.
He is a full professor for Computer Science at Saarland University (since 2009), Head of the Ubiquitous Media Technology Lab and scientific director of the Innovative Retail Laboratory (IRL) at DFKI. Prof. Krueger is an internationally renowned expert on Man-Machine-Interaction and Artificial Intelligence. In 2010 he has established the Mediainformatics study programme at the Saarland University and directs it to this day.
Antonio is a co-founder of the Saarbrücken-based technology company Eyeled GmbH, which focuses on the development of mobile and ubiquitous information systems. Many of his research findings have found their way into applications in retail and other industrial domains. From 2004 to 2009 he was a professor of computer science and geoinformatics at the University of Münster and acted as the managing director of the institute for geoinformatics. He studied Computer Science and Economics at Saarland University and finished his Ph.D in 1999 as a member of the Saarbrücken graduate school of „Cognitive Science“.