New hybrid approaches to Precision Medicine: machine learning, generative AI, and digital twins


University Hospital Campus, Linköping University
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New hybrid approaches to Precision Medicine: machine learning, generative AI, and digital twins

November 5, 2024 @ 11:30 17:00 CET

Precision medicine and diagnostics is becoming a vitally needed technology to meet tomorrow’s healthcare challenges. Important revolutions in machine learning now allow us to analyze images and multi-omics data and aid in both diagnosis, prognosis, and tailoring of personalized treatments. Similarly, recent breakthroughs in generative AI allow us to use new interfaces and input sources, using e.g. advanced language models. Finally, wearable sensors and a variety of eHealth apps allow us to get much-needed information about what an individual is doing, which opens the door to personalized predictions beyond the use of risk factors and covariates. However, the combination of all of these disparate data sources, where different sets of variables are measured in each source, requires hybrid approaches, where knowledge-based digital twins are combined with ML and generative AI. In this mini-symposium, we will focus on all these new emerging technologies and on their hybrid combinations. The event features keynote presentations, shorter presentations selected from abstracts, and ample opportunities for networking, e.g. around a poster session. 

Mark your calendars! A detailed program will be available shortly.

We look forward to welcoming you to Linköping!

Members of The Data-driven Precision Medicine & Diagnostics expert group:

Gunnar Cedersund, Linköping University
Sven Nelander, Uppsala University
Lars Klareskog, Karolinska Institutet
Johan Trygg, Umeå University
Patrik Georgii-Hemming, Karolinska Institutet
Päivi Östling, KI (adj. SciLifeLab Precision Medicine Capability lead)
Francis Lee (adj. WASP-HS representative in DDLS)
David Gisselsson Nord (adjunct as GMS representant)
Janne Lehtiö, chair (DDLS SG member)

Last updated: 2024-04-26

Content Responsible: Erika Erkstam(