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Related Experiment Video

Updated: Jun 16, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

The Effect of Structured Context on Chest Radiograph Interpretation by a Multimodal Large Language Model: A Pilot

Andrew Cusick¹, Shelby Guy¹, Christopher Herz¹

¹Radiology, Rocky Vista University College of Osteopathic Medicine, Parker, USA.

|June 15, 2026

Summary

This summary is machine-generated.

Structured guidance improved ChatGPT

Keywords:

artificial intelligence in radiology chatgpt chest x-ray deep learning artificial intelligence msk radiology musculoskeletal radiology

Related Experiment Videos

Last Updated: Jun 16, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

Artificial Intelligence in Radiology
Medical Image Interpretation
Large Language Models

Background:

Multimodal large language models (LLMs) show promise for medical image interpretation.
The impact of structured contextual guidance on LLM performance in radiology is not well understood.

Purpose of the Study:

To evaluate if structured contextual guidance enhances ChatGPT's performance in interpreting chest radiographs.
To assess the reliability of LLMs for diagnostic tasks in medical imaging.

Main Methods:

A comparative pilot study involving 50 chest radiographs with known diagnoses.
ChatGPT was tested in a baseline condition (standard prompt) and a structured-context condition (teaching module + radiologic framework).
Performance was measured using percent-correct scoring with partial credit for appropriate reasoning.

Main Results:

Overall accuracy increased from 28% to 62% with structured guidance.
100% accuracy was achieved for COPD, pulmonary edema, and foreign body identification.
Performance was limited for lung cancer (20%), feeding tubes (40%), and rib fractures; atelectasis accuracy decreased.

Conclusions:

Structured contextual guidance significantly improved ChatGPT's performance on specific chest radiograph interpretation tasks.
Current LLM diagnostic reliability is insufficient for independent clinical use.
Further research with larger studies, standardized scoring, and external validation is necessary.