Blogs on AI in Medicine » Lamarr Institute

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From Cloud to Edge: Running a Real-Time AI Support Agent in the Trauma Room

Jasper Schulze Buschhoff,

20. August 2026

Can real-time AI in a trauma room combine privacy, low latency and high quality? TraumaAgent shows what it takes to move an agentic system from cloud infrastructure to the edge. ...

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Artificial Intelligence in Parkinson’s Research

Prof. Dr. Holger Fröhlich,

13. May 2026

Could Artificial Intelligence transform the future of Parkinson’s care? This article explores how trustworthy AI methods may enable earlier diagnosis, more personalized treatments, and improved prediction of disease progression....

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AI and ML applied to Life Sciences

Dr. Elena Xerxa,

06. November 2024

Explore how AI and machine learning transform life sciences by enhancing transparency in drug discovery and advancing chemical and biological knowledge through interdisciplinary research at the Lamarr Institute....

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Clustering for recognizing medical patterns: Gaussian Mixture Models explained

Lea Nguyen,

24. April 2024

Medical data often hides patterns that are difficult to recognize but relevant for diagnostics and therapy. How can we identify these structures? The clustering algorithm Gaussian Mixture Model provides an answer....

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Insights from the AAAI XAI4Sci Explainable Machine Learning for Sciences Workshop

Katharina Beckh,

02. April 2024

Enabling new discoveries with explainable machine learning – how the sciences engage with explainability is described in our blog entry which summarizes the key insights of the AAAI XAI4Sci workshop. ...

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How does Artificial Intelligence change medical care and diagnosis?

Dario Antweiler,

24. August 2022

In cooperation with the Landesfrauenrat RLP: This article takes a look behind the scenes of Artificial Intelligence in medicine and shows how AI is changing medical care and diagnosis....

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AI in radiology – Classifying and explaining hemorrhagic strokes with deep learning

Rebekka Görge,

27. July 2022

If a stroke occurs, rapid intervention is required. Deep learning classification could support doctors. However, in safety-critical areas such as medicine, AI predictions must be transparent....

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An Approach to Granular Testing of Deep Neural Networks

Sujan Sai Gannamaneni,

01. June 2022

Establishing rigorous testing and certification techniques is essential before deploying new technologies like Deep Learning (DL) in safety-critical applications. We propose a testing approach that could identify weaknesses in DL models....

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Better assessment of disease progression with ranking SVM

Sabine Kugler,

30. June 2021

In addition to objective patient indicators, clinical studies often contain subjective expert assessments of disease status. In order to reduce fluctuations in expert knowledge, the existing assessments are combined with measured indicators using a ranking SVM....