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DTSTART;TZID=Europe/Stockholm:20260701T090000
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UID:submissions.pasc-conference.org_PASC26_sess154@linklings.com
SUMMARY:MS4F - HPC and the Health Sciences: Co-Design for Trust, Robustnes
 s, and Communication
DESCRIPTION:Access the recording\n\nOrganizer(s): Justin M. Wozniak, Thoma
 s Brettin (Argonne National Laboratory, University of Chicago), and Eric S
 tahlberg (MD Anderson)\n\nHPC-powered AI applications are increasingly acc
 urate and robust, but challenges remain when translating these capabilitie
 s into real-world prevention and treatment routines. Central to this issue
  is the notion of trust, which is essential to all stakeholders in the hea
 lth care complex: patients, providers, management, and governance. Trustwo
 rthy AI systems use transparent reasoning processes, are explainable, acco
 untable, robust, fair, honest, privacy-preserving, and amenable to human g
 oals. In the context of health, all of these aspects are potential blocker
 s to future adoption. In this minisymposium, we will bring in experts from
  AI model development, health systems analysis, and the clinical translati
 on of AI-integrated cancer treatments.\n\nComputational Prediction of Anti
 -Cancer Drug Response in Preclinical Cancer Models Using Deep Learning and
  High-Performance Computing\n\nCancer is a complex and heterogeneous disea
 se. Tumors of the same histological type can respond differently to the sa
 me anti-cancer therapy. Therefore, accurate prediction of anti-cancer drug
  response is of paramount importance for both patient treatment design and
  therapeutic development. We have d...\n\n\nYitan Zhu, Justin Wozniak, Ale
 xander Partin, and Thomas Brettin (Argonne National Laboratory) and Rick S
 tevens (Argonne National Laboratory, The University of Chicago)\n---------
 ------------\nCo-Designing Trustworthy Scientific Agents: From Closed-Loop
  Validation to Self-Improving Discovery\n\nThe Transformational AI Models 
 Consortium (ModCon) is a foundational initiative of the Genesis Mission, a
 dvancing the nation's scientific and technological resources to develop an
 d deploy AI models that revolutionize scientific discovery. In this talk, 
 I'll discuss how we co-design trustworthy scien...\n\n\nNeeraj Kumar (Paci
 fic Northwest National Laboratory)\n---------------------\nTrust and Trans
 parency From Pipeline to Practice: Foundations for Robust Clinical AI\n\nD
 eployment of AI in clinical environments demands more than computational c
 apabilities, it requires systematic commitment to data integrity and a del
 iberate strategy for building clinician trust. This talk examines two inte
 rdependent pillars of responsible clinical AI: the critical role of data i
 n c...\n\n\nCaroline Chung (UT MD Anderson Cancer Center)\n---------------
 ------\nHow to Predict Immunotherapy Using AI in Clinical Practice in NSCL
 C: The I3LUNG and APOLLO 11 Examples\n\nRecent initiatives, including I3LU
 NG and APOLLO-11, have demonstrated the potential of AI-driven models to i
 ntegrate multidimensional data from clinical records, imaging, pathology, 
 and molecular profiling to better predict treatment response and survival 
 outcomes. These approaches use advanced mach...\n\n\nArsela Prelaj (Nation
 al Cancer Institute (IRCCS))\n\nDomain: Applied Social Sciences and Humani
 ties, Life Sciences, Computational Methods and Applied Mathematics\n\nSess
 ion Chairs: Justin M. Wozniak (Argonne National Laboratory, University of 
 Chicago) and Thomas Brettin (Argonne National Laboratory, University of Ch
 icago)
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