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Automating Lung-RADS Categorization And Follow-Up Recommendations Using In-Context Learning With Large Language

Tiancheng Zhou¹, Aokun Chen¹, Yu Hu¹

¹Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 23, 2026

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This summary is machine-generated.

Large language models (LLMs) can help radiologists by automating lung nodule categorization from radiology reports. This improves efficiency and accuracy in lung cancer screening, aiding timely interventions.

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Lung cancer is a leading cause of mortality, necessitating early detection through screening.
Low-dose computed tomography (LDCT) screening is effective for high-risk individuals.
Interpreting radiology reports for lung nodules is time-consuming and can be ambiguous, even with Lung-RADS.

Purpose of the Study:

To develop an in-context learning framework using large language models (LLMs) to streamline lung nodule assessment.
To identify an optimal LLM approach for accurate lung nodule categorization and management decisions.
To provide robust and interpretable decision support for radiologists in lung cancer screening.

Main Methods:

Utilized a large language model (LLM) framework with in-context learning.

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Focused on processing original radiology reports for lung nodule analysis.

Aimed to generate Lung-RADS assessments and support clinical decision-making.

Main Results:

The LLM framework demonstrated potential in categorizing lung nodules from reports.
The study explored methods to enhance accuracy and interpretability of LLM outputs.
The research focused on reducing radiologist workload in lung cancer screening.

Conclusions:

LLMs offer a promising solution to challenges in interpreting lung cancer screening reports.
This approach can enhance efficiency and accuracy in radiologist workflows.
The framework supports timely and precise interventions for lung cancer patients.