Work

Large Language Models for Structured Chest CT Reporting in Portuguese: A Comparative Study with Radiologist Validation

Year: 2026

Type: conference-paper

Abstract: To evaluate LLMs for converting free-text Portuguese chest CT reports into structured JSON for clinical communication and data reuse. Gemini 1.5 Flash, GPT-4o, and LLaMA 3.3 were tested on 1,102 de-identified reports using a dynamic JSON template with few-shot prompting. Validation combined radiologist review and quantitative metrics. All models produced coherent structured outputs. Gemini achieved the best agreement (macro-F1 0.852; micro-F1 0.853), followed by LLaMA (0.806; 0.809) and GPT-4o (0.797;0.798). LLM-assisted structuring of Portuguese chest CT reports is feasible and attains high agreement with manual references; section-aware prompting and JSON validation improve robustness.

DOI: 10.5753/sbcas.2026.20861

Authors: Juliana Petruceli, Marcelo Oliveira, Tarcisio Ferreira, Jose Arthur Sabino

Institution: Universidade Federal de Alagoas

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

OpenAlex ID: W7165679643

FWCI:

Cites: 18

Cited by:

Related to: