Use of Artificial Intelligence in epidemiological data for Decision-Making in Health
DOI:
https://doi.org/10.33448/rsd-v15i1.50563Keywords:
Artificial Intelligence, Epidemiology and Biostatistics, Decision Making, Clinical Medicine, Use of Scientific Information in Health Decision-Making.Abstract
Objective: To evaluate the feasibility of using artificial intelligence (AI), via the ChatGPT o3 Mini High model, to analyze epidemiological mortality data from traffic accidents and provide support for decision-making in health. Methodology: This is an observational, descriptive, exploratory study using secondary data from the Mortality Information System (SIM) and from the State Health Secretariat of Minas Gerais, regarding deaths by external causes (ICD-10 V01–V99) between 2010 and 2024. The dataset underwent cleaning and filtering, then was enriched with structured prompts in the ChatGPT o3 Mini High to cross variables and identify demographic, temporal, and accident-type patterns. Results: A total of 219 traffic-accident death records from the Itajubá-MG region and surrounding municipalities were analyzed. The AI model performed descriptive and correlational analyses and suggested interventions such as targeted educational campaigns, road infrastructure improvements, and directed enforcement. Findings included a predominance of deaths among men aged 20–39, seasonality in festive periods, and higher incidence of motorcycle collisions and pedestrian strikes. Conclusion: The application of ChatGPT o3 Mini High proved viable as a tool to support epidemiological analysis of traffic mortality. Its outputs suggest potential to guide more assertive health policies. Future work should expand to other regional datasets, test reproducibility, and train local health managers to use this technology.
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