ScaDS.AI announces and welcomes you to join our public colloquium session on Wednesday, Mar 01, 2023 14:00-16:00 CET at Leipzig University, Paulinum, Felix Klein lecture hall (P 501/502) and in parallel online (link to Zoom session).
University of Hamburg, Center for Bioinformatics
Privacy-preserving Systems Medicine – OR – What I learnt about Arnold Schwarzenegger while studying breast cancer
Large-scale omics data analyzed by artificial intelligence (AI) technology is finding its way into the clinic to revolutionize our approach to medicine as a whole. Beyond personalized medicine, AI-driven medical data profiling is leaving massive footprints – from drug repurposing to a mechanistic redefinition of diseases. To move away from organ- and symptom-based disease descriptors to clinically actionable mechanistic approaches, computational systems and network medicine emerged. We will introduce the field, discuss current approaches and pitfalls as well as potential future avenues. While we first focus on breast cancer to illustrate the principles of systems/network medicine – as a running example – along the talk, we will change our focus to arbitrary diseases to demonstrate the potential and scalability of the field and our own work.
University of Bonn & DZNE, Bonn
Swarm Learning, a democratizing approach for the application of AI to medical data
Developing precision medicine principles is one of the major goals in the current transformation of the health care sector. While medicine traditionally is an expert- and knowledge-driven discipline, the availability of medical data at the highest resolution will allow a complementary use of big data to define disease-specific patterns that can help physicians to guide diagnostics and treatments. However, to make use of medical big data, it will be necessary to utilize artificial intelligence and machine learning approaches. Major tasks for the translation of the many exciting AI research projects in medicine to clinical applications include generalization of AI models, scaling of these models and real-world testing across institutions as well as protecting data privacy and ensuring data security in a sustainable fashion. To address these tasks, we have recently developed Swarm Learning, the world-wide first combination of a private-permissioned blockchain with AI applications. We illustrated with several use cases that Swarm Learning addresses all these tasks, outperforms local and even central AI models and thereby provides a democratized approach to AI applications in medicine.
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