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SIIM Annual Meeting
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SIIM Annual Meeting
  • Attend
    • Who Attends
    • Networking Events
    • Hotels & Venue
    • Preliminary Program
    • Press Registration
    • Justification Letter & Tips
    • Health & Safety
  • Program
    • Program Agenda
    • Featured Education
      • General Sessions
      • Learning Labs
      • Scientific Presentations
    • Learning Zones
      • AI Playground
      • Hackathon
      • Learning Theaters
      • Ask the Informaticists
    • Continuing Education
    • Speakers List
    • Preliminary Program
  • Tech Expo
    • InformaticsTECH Day
    • Exhibitors
    • Alphabetical Exhibitor List
    • Product Categories
  • Pre-Conference
    • Epic Users Group Meeting
    • SIIM-ACR Data Science Summit
  • Exhibit+Sponsor
    • Exhibit Signup
    • Sponsor + Advertise
    • Booth Contract
    • Rules & Regulations
    • Exhibitor Resources
    • Vendor Prospectus
    • Suitcase Marketing Policy
  • Register Now
Headshot of Shahriar Faghani
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Event Speaker

Shahriar Faghani, MD

Assistant Professor of Radiology
Mayo Clinic

Shahriar Faghani is an incoming radiology resident at the University of Pennsylvania, and an adjunct Assistant Professor of Radiology at the Radiology Informatics Lab, Mayo Clinic in Rochester. Using multi-modal deep learning, he develops computer-aided diagnostic/prognostic tools for real-world neuroradiology, musculoskeletal radiology, and digital pathology challenges. His research interests include uncertainty quantification to enhance the trustworthiness of machine predictions and exploring the potential of generative models in radiology. He serves on SIIM, ASNR, ASSR, ASFNR, ASHNR, and MONAI Deploy committees. 

Sessions

Collaborative Intelligence: Multi-Agent Large Language Models in Medical Imaging AI Learning Lab

Presented by Mana Moassefi, MD, Pouria Rouzrokh, MD, MPH, MHPE, Shahriar Faghani, MD May 23, 2025 | 7:30 AMRoom B117

As the field of medical imaging evolves, the integration of multi-agent large language models (LLMs) presents groundbreaking opportunities to enhance diagnostic accuracy and streamline clinical workflows. This interactive session provides a deep dive into how multi-agent LLMs collaborate to process and analyze medical imaging data, from system construction to real-world optimization.   Attendees will gain hands-on…

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