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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
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Related Experiment Video

Updated: May 21, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Human-in-the-Loop Large Language Model-Augmented Diagnostic Reasoning in Thoracic Imaging: Impact of Radiologic

Jiyoung Song1, Hongseok Ko1, Dae Hee Han2

  • 1Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

AJR. American Journal of Roentgenology
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) improved diagnostic accuracy in radiology through text-based workflows. Reader expertise significantly influenced LLM performance and adoption, highlighting the importance of human-in-the-loop interactions.

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Last Updated: May 21, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Radiology Workflow Optimization

Background:

  • Technical and regulatory hurdles limit large language model (LLM) integration in diagnostic radiology.
  • Reader-mediated, text-based workflows offer a practical alternative for LLM-assisted diagnostic reasoning.

Purpose of the Study:

  • To assess the impact of LLM assistance on diagnostic performance using reader-generated free-text descriptions.
  • To evaluate how reader expertise affects LLM-augmented diagnostic workflows in radiology.

Main Methods:

  • Retrospective analysis of 93 thoracic radiology cases (radiography, CT, MRI, PET/CT).
  • Ten readers (5 thoracic radiologists, 5 residents) performed independent interpretations and provided free-text findings.
  • LLM (Gemini 3.0 Pro) processed text descriptions to rank differential diagnoses; readers then re-evaluated diagnoses with LLM output.

Main Results:

  • LLM accuracy was higher with reader descriptions (63.9%) than with images alone (52.7%).
  • Diagnostic accuracy improved for both radiologists (56.3% to 65.6%) and residents (42.4% to 58.5%) after LLM assistance.
  • Residents showed greater improvement and higher adoption of LLM-favored diagnoses, but also a greater rate of switching to incorrect diagnoses based on misleading LLM output.

Conclusions:

  • Text-based LLM assistance enhances reader diagnostic accuracy in radiology.
  • The effectiveness of LLM-augmented workflows is significantly moderated by reader expertise.
  • Human-in-the-loop interactions are crucial, with operator expertise shaping LLM input and output evaluation.