NIAR-Saúde

Center for Responsible Artificial Intelligence in Health

Interdisciplinary research bringing computing and health together, turning data into reliable solutions.

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Featured

Latest news & events

UFMG’s secure room for handling sensitive health data

Media

March 10, 2026

UFMG becomes the first university in Brazil with a secure room for sensitive health data

The country’s first such environment enables sensitive-data processing and AI solutions to improve diagnoses, prognoses and treatments.

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Inauguration of NIAR-Saúde’s Secure Room at UFMG’s Medical School

Event

March 09, 2026

NIAR-Saúde’s Secure Room is inaugurated to expand research with AI and health data

New space at UFMG’s Medical School lets AI analyses and models for health be developed in a controlled, monitored and auditable environment.

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Our pillars

Innovation in responsible data and artificial intelligence for health

NIAR-Saúde's work rests on three pillars that bring computing and health together: governance for the responsible use of artificial intelligence, the data and models that make solutions reliable, and the applications that take all of this into real-world health practice.

Governance

Structuring processes and practices for the responsible use of artificial intelligence, focusing on quality, risk management, transparency and continuous governance.

Data and Models

Development and operation of data and artificial intelligence models, covering data engineering, building, evaluation and continuous monitoring of solutions.

Applications

Applying data and models in health contexts, focusing on impact, cost-effectiveness and decision-making support across different scenarios.

Publications

Recent work

Recent articles and scientific output from the group.

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2026 Goal 1

Quem controla os dados? Governança e Responsabilidade na Era da Inteligência Artificial

Carvalho, M., Azevedo, K., Rocha, L., Vasconcelos, M., Brandão, M., Meira, W.

SBC Horizontes, ISSN 2175-9235, April 2026

DOI

2025 Goal 4

Artificial intelligence in the electrocardiogram: automatic diagnosis of the normal ECG in a Tele-electrocardiogram service

Paixão, G., Abreu, P.E., Gomes, P.G., Schön, T.B., Ribeiro, A.H., Ribeiro, A.L.P.

European Heart Journal, Volume 46, Issue Supplement_1, November 2025, ehaf784.4407

DOI

2025 Goal 5

Use of Machine Learning to Predict the Consumption of Fruits and Vegetables in Small Areas

Gomes, C.S., Araújo, L.F., Faria, T.M.T.R., Bernal, R.T.I., Souza, J.B., Alves, S.N., Barbosa, B.R.G., Cardoso, L.S.M., Gonçalves, M.A., Almeida, J.M., Malta, D.C.

Ciência e Saúde Coletiva, November 2025

DOI

2025 Goal 4

High-precision automatic classification of normal electrocardiograms: An AI-based model for the telehealth system

Abreu, P.E.O.G.B., Ribeiro, A.H., Paixão, G.M.M., Schön, T.B., Gomes, P.R., Ribeiro, A.L.P.

Journal of Electrocardiology, Volume 91, July–August 2025, 153988

DOI

Team