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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Clinical Decision Support Systems

Background:

Multimodal generative artificial intelligence (AI) offers potential for preliminary radiology report generation.
Reader studies are essential to validate the clinical utility of AI in radiology.
Domain-specific AI tools require rigorous assessment for practical application.

Purpose of the Study:

To evaluate the clinical value of a specialized multimodal generative AI tool for interpreting chest radiographs.
To assess the impact of AI-generated preliminary reports on radiologist performance through a reader study.

Main Methods:

A retrospective reader study involving 758 chest radiographs and five radiologists.
Radiologists interpreted images with and without AI-generated preliminary reports.
Evaluated reading times, reporting agreement (RADPEER), and quality scores; analyzed factual correctness, sensitivity, and specificity on a subset.

Main Results:

AI reports significantly decreased average reading times (34.2s to 19.8s, P < .001).
Report agreement and quality scores showed significant improvement with AI assistance (P < .001).
Sensitivity for detecting abnormalities like widened mediastinal silhouettes and pleural lesions increased notably.

Conclusions:

The integration of a domain-specific multimodal generative AI model enhances efficiency and quality in radiology report generation.
AI-assisted interpretation leads to faster reading times and improved diagnostic accuracy.
While overall performance improves, individual radiologist variability persists.