Large Language Models & Report Generation | Scientific Abstract Presentations

A Schema-Constrained Hybrid Regex–Large Language Model Pipeline for Reliable Structured Extraction of Interventional Radiology Reports (opens in new tab) 

  • Seema Singh Saharan, PhD, UCSF 
  • Jhaydine Bandola; Rachel Johansson, MD; Pete Lokken, MD, MPH; Beck Olson; Rajesh Shah, MD; Alexandra So; Harsh Tripathi; Xiao Wu, MD 

 

Large Language Model-Based Automated Detection and Turnaround Time Monitoring of Critical Results in a Pediatric Radiology Workflow (opens in new tab) 

  • Pooja Pathak, MS, Lucile Packard Children's Hospital Stanford 
  • Christina Vernazza; Linn Dee Doyle; Zachery Seow, MBA, CRA, R.T.(R)(CT)(MR)(ARRT); Paul Fong; Timothy Mak, MS; Richard Barth, MD; Ali Syed, MD; Shreyas Vasanawala, MD, PhD; Sergios Gatidis, MD 

 

Personalizing Radiology Impression Generation to Radiologist Style and Subspecialty with Auditable, Auto-Generated Playbooks(opens in new tab) 

  • Ushanandini Raghavan, PhD, DeepHealth 
  • Madhu Jahagirdar 

 

Residents as a Proxy for AI Draft Reporting: LLM-based Analysis(opens in new tab) 

  • Steven Rothenberg, MD, Thomas Jefferson University 
  • Shahir Monsuruddin, MD; Paras Lakhani, MD; Trevor Vent, PhD; Baskaran Sundaram, MD; Adam Flanders, MD, FSIIM 

 

SpineGraph: Vertebral-Level-Aware Evaluation for Automated Cervical Spine Report Generation (opens in new tab)

  • Sydney A. Levin, Tulane University 
  • Matheus Ferreira; Eduardo Farina, MD; Ronald Peshock, MD; Paulo Kuriki, MD 

 

What Automated Radiology-Report Metrics Can and Cannot Detect: A Controlled Simulation Study of Single-Word Clinical Changes (opens in new tab)

  • Mohammadreza Chavoshi, MD; Emory University School of Medicine 
  • Bardia Khosravi, MD, MPH, MHPE; Frank Li, PhD; Theo Dapamede, MD, PhD; Janice Newsome, MD; Hari Trivedi, MD; Judy Gichoya, MD, MS, FSIIM 

Learning Objectives

Upon completion of the scientific sessions, participants will be able to: 

  • Evaluate emerging AI methods and applications in medical imaging research.  
  • Assess the performance, reliability, and clinical relevance of medical imaging AI across diverse applications and settings.  
  • Identify opportunities and challenges associated with translating medical imaging AI research into clinical practice and patient care. 

SESSION ID

1002


DATE

MON, OCT 26


TIME

9:00 AM – 10:30 AM ET


LOCATION

Arthur H. Rubenstein Auditorium


CONTINUING EDUCATION

ASRT-RT | CAMPEP-MPCEC | SIIM IIP-CIIP