Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation - Takeaways - MDSpire

Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation

  • By

  • Songsoo Kim

  • Seungtae Lee

  • See Young Lee

  • Joonho Kim

  • Keechan Kan

  • Hyunji Lee

  • Dukyong Yoon

  • June 4, 2026

  • 0 min

Share

  • 1

    Large language models (LLMs) face challenges in radiology report proofreading due to low positive predictive value (PPV) and high false alarm rates.

  • 2

    The proposed multipass LLM framework aims to enhance precision and efficiency in error detection while addressing computational cost concerns.

  • 3

    The framework includes a lightweight report extractor, stepwise reasoning for error detection, and a user interface for radiologist review.

  • 4

    A benchmark study utilized 1000 error-free radiology reports to validate the framework's performance against nonoptimized baselines.

  • 5

    The study emphasizes the need for strategies that improve PPV in low-error settings and explore operational cost-efficiency in AI applications.

Original Source(s)

Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation

  • by Songsoo Kim, Seungtae Lee, See Young Lee, Joonho Kim, Keechan Kan, Hyunji Lee, Dukyong Yoon

  • June 4, 2026

Related Content

Rim enhancement on MRI predicts long-term outcomes in extruded/migrated lumbar disc herniation managed with an inflammation-preserving protocol: a 6-year prospective cohort study

Differential diagnosis of testicular embryonal rhabdomyosarcoma and testicular seminoma with enhanced CT: a retrospective study

Utilizing CT chest imaging for the diagnosis and monitoring of COVID infections and emergency intensive care unit strategic treatment