17.12.2024
©TUM
Daniel Rückert Advances Stroke Diagnosis With AI Innovation
TUM News
MCML Director Daniel Rückert and his collaborators have achieved a groundbreaking milestone in medical AI. Their innovative model uses machine learning to precisely determine the timing of strokes based on CT imaging. This advancement is critical for improving stroke diagnosis and ensuring patients receive timely and effective treatment. By addressing a long-standing challenge in neurology, this research exemplifies the transformative potential of AI in healthcare.
«We believe that our model is so powerful because it not only assesses how dark the damaged region is, but also includes additional features from the scans, such as texture, and accounts for variations within the damaged areas and background.»
Daniel Rückert
MCML-Director
Related
18.12.2025
"See, Don’t Assume": Revealing and Reducing Gender Bias in AI
ICLR 2025 research led by Zeynep Akata’s team reveals and reduces gender bias in popular vision-language AI models.
16.12.2025
Fabian Theis Featured in Handelsblatt on the Future of AI in Precision Medicine
MCML PI Fabian Theis discusses AI-driven precision medicine and its growing impact on individualized healthcare and biomedical research.
16.12.2025
Gitta Kutyniok Featured in VDI Nachrichten on AI Ethics
Gitta Kutyniok discusses measurable criteria for ethical AI, promoting safe and responsible autonomous decision-making.
16.12.2025
Hinrich Schütze Featured in WirtschaftsWoche on Innovative AI Approaches
Hinrich Schütze discusses Giotto.ai’s efficient AI models, highlighting memory separation and context-aware decoding to improve robustness.