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DTSTAMP:20260421T090514Z
LOCATION:Bldg. 8 - Room B 101
DTSTART;TZID=Europe/Stockholm:20260629T133000
DTEND;TZID=Europe/Stockholm:20260629T153000
UID:submissions.pasc-conference.org_PASC26_sess110@linklings.com
SUMMARY:MS1G - Advancing Medical AI: Challenges for Developing AI-Driven I
 n-Silico Clinical Trials for Accelerating Translational Medicine
DESCRIPTION:Artificial intelligence (AI) has achieved major success in med
 icine, particularly in diagnostic imaging, pathology classification, and c
 linical report generation, accelerating research translation and improving
  care. However, most deployed systems remain task-specific, lack biomedica
 l reasoning, and generalize poorly across data modalities and clinical set
 tings. The Advancing Medical AI minisymposium explores how emerging approa
 ches, especially large multimodal models (LMMs), can enable AI-driven in-s
 ilico clinical trials (ISCTs) that better connect research innovation with
  clinical application. Recent LMMs integrate medical images, text, and str
 uctured data to support diagnosis, segmentation, and reporting, enabling t
 he simulation of biological and clinical processes and advancing virtual p
 atient modeling. Key challenges remain in explainability, computational ef
 ficiency, privacy protection, and integration with hospital infrastructure
 , highlighting the need for transparent data governance and verifiable sys
 tems. In parallel, ISCTs are gaining momentum as computer-based experiment
 s that model disease progression and therapy response in virtual patient c
 ohorts. Built on digital twins-dynamic computational models continuously u
 pdated with clinical data, ISCTs promise lower costs, faster development, 
 and improved safety. Despite their potential, barriers such as data hetero
 geneity, limited interpretability, validation gaps, regulatory constraints
 , and infrastructure demands persist.\n\nDigital Twins Meet the HPC Contin
 uum: Distributed Systems Challenges for Scalable and Privacy-Aware In-Sili
 co Medicine\n\nThe convergence of AI and computational modeling is redefin
 ing scientific workflows, moving from centralized HPC platforms toward a d
 istributed, heterogeneous computing continuum. In healthcare, this evoluti
 on is exemplified by medical digital twins, which integrate multimodal pat
 ient data, simulati...\n\n\nFrédéric Le Mouël (University of Lyon, INSA Ly
 on; CITI Laboratory)\n---------------------\nHybrid Imitation–Reinforcemen
 t Learning for Stroke Rehabilitation: Toward Adaptive and Human-Compatible
  VR Therapy\n\nStroke rehabilitation is not just about repeating movements
 , it is about helping patients gradually regain control, confidence, and n
 atural motor behavior. While virtual reality has opened new possibilities 
 for immersive therapy, current systems still struggle to adapt to individu
 al patients and oft...\n\n\nMahdiyeh Moosavi (LISPEN Arts et Métiers Insti
 tute of Technology Chalon-Sur-Saône, France)\n\nDomain: Engineering, Life 
 Sciences, Computational Methods and Applied Mathematics\n\nSession Chairs:
  John Anderson Garcia Henao (University of Bern, ARTORG Center for Biomedi
 cal Engineering Research) and Carlos Barrios Hernandez (Universidad Indust
 rial de Santander, LIG/INRIA - CITI Laboratory)
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