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Comparison of AI Models for Data Extraction in Glucometers

Year: 2025

Type: article

Abstract: Diabetes is a chronic condition that requires continuous monitoring of blood glucose levels, making glucose meters essential tools for managing patients’ health. This study compares three Artificial Intelligence models — Gemini, GPT-4o, and Llava 1.5 — to identify which one extracts glucose, date, and time data from these devices’ images with greater accuracy and efficiency. Through prompt engineering techniques, the aim is to optimize the extraction process, making it more reliable and automated, contributing to more precise and accessible glucose monitoring.

Source: Revista de Informática Teórica e Aplicada

DOI: 10.22456/2175-2745.142648

Authors: Genivaldo Braynner Teixeira do Carmo, Julyanne Maria Dos Santos Correia, Ronaldo Rodrigues da Silva Filho, Pablo Azevedo Sampaio, Robson Wagner Albuquerque de Medeiros

Institution: Universidade Federal Rural de Pernambuco

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4415968812

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