From Data to Discovery: Harnessing AI in Medicine for Improved Patient Care


  • Datum: 22.09.2023
  • Uhrzeit: 10:30 - 12:00
  • Vortragender: Prof. Lars Kaderali
  • Prof. Koderali is a director of the Institute for Bioinformatics at Greifswald University. He made his master in computer science in the University of Cologne in 2001 and got his PhD from the same university in bioinformatics in 2006. He worked in Heidelberg and Dresden before acquiring a chair of bioinformatics in Greifswald in 2015. He serves as an editor in PLoS one and a chief editor in Frontiers in Virology. He was a member of the expert council in COVID-19of the German Federal Chancellor.
  • Ort: IPP Greifswald
  • Raum: Günter-Grieger-Lecture Hall (Greifswald)
  • Gastgeber: IPP
  • Kontakt:
From Data to Discovery: Harnessing AI in Medicine for Improved Patient Care

The healthcare system in Germany and other western countries is faced with significant challenges as the population ages and the prevalence of complex diseases rises, while the number of healthcare professionals available to treat and care for patients dwindles. In this talk, I explore the transformative potential of Artificial Intelligence (AI) in addressing this situation. I will show several examples on how AI can help in medicine, from basic research to improved prevention, diagnosis and treatment. Greifswald University is well known for the Study of Health in Pomerania, a comprehensive epidemiological study with the largest examination program worldwide, and with over 25 years of follow-up available. This data set provides a rich foundation for exploring AI's role in identifying risk factors for common diseases. I will show an example of how we use regularized Bayesian networks in unraveling intricate interactions between these risk factors and their link with disease. In a second example, I will show how machine learning approaches can help to understand ageing. By developing and analyzing age clocks that are trained on molecular data, we showcase how AI can unravel the complex mechanisms underlying aging and its associated diseases. Furthermore, I show examples of our work in predicting therapy outcome in cancer patients, shedding light on personalized medicine's potential.

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