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Review

LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings

by
Fabiano Tonaco Borges
1,2,*,
Gabriela do Manco Machado
3,
Maíra Araújo de Santana
1,
Karla Amorim Sancho
1,
Giovanny Vinícius Araújo de França
2,
Wellington Pinheiro dos Santos
1 and
Carlos Eduardo Gomes Siqueira
4
1
Department of Biomedical Engineering, Geoscience and Technology Center, Federal University of Pernambuco (UFPE), Recife 50740-550, PE, Brazil
2
Department of Science and Technology, Vice-Ministry of Science, Technology and Innovation, Brazilian Ministry of Health, Brasília 70719-040, DF, Brazil
3
School of Dentistry, University of São Paulo (USP), São Paulo 05508-000, SP, Brazil
4
School for the Environment, University of Massachusetts Boston (UMass Boston), Boston, MA 02125, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2026, 23(1), 81; https://doi.org/10.3390/ijerph23010081
Submission received: 22 October 2025 / Revised: 19 December 2025 / Accepted: 5 January 2026 / Published: 7 January 2026
(This article belongs to the Section Global Health)
Review Reports Versions Notes

Abstract

Artificial intelligence (AI) has become a strategic technology for global health, with increasing relevance amid the climate emergency and persistent digital inequalities. This study examines how AI has been applied in Brazilian healthcare through a scoping review with an in-depth methodological synthesis, focusing on Transfer Learning (TL) and Federated Learning (FL) as approaches to address data scarcity, privacy, and technological dependence. We searched PubMed, SciELO, and the CNPq Theses and Dissertations Repository for peer-reviewed studies on AI applications in Brazil, screened titles using AI-assisted tools with manual validation, and analyzed thematic patterns across methodological and infrastructural dimensions. Among 349 studies retrieved, six explicitly used TL or FL. These techniques were frequently implemented through multi-country research consortia, demonstrating scalability and feasibility for collaborative model training under privacy constraints. However, they remain marginal in mainstream practice despite their ability to deploy AI solutions with limited computational resources while preserving data sovereignty. The findings indicate an emerging yet uneven integration of resource-aware AI in Brazil, underscoring its potential to advance equitable innovation and digital autonomy in health systems of the Global South.
Keywords: artificial intelligence; machine learning; transfer learning; federated learning; health systems artificial intelligence; machine learning; transfer learning; federated learning; health systems

Share and Cite

MDPI and ACS Style

Borges, F.T.; Machado, G.d.M.; Santana, M.A.d.; Sancho, K.A.; França, G.V.A.d.; Santos, W.P.d.; Siqueira, C.E.G. LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings. Int. J. Environ. Res. Public Health 2026, 23, 81. https://doi.org/10.3390/ijerph23010081

AMA Style

Borges FT, Machado GdM, Santana MAd, Sancho KA, França GVAd, Santos WPd, Siqueira CEG. LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings. International Journal of Environmental Research and Public Health. 2026; 23(1):81. https://doi.org/10.3390/ijerph23010081

Chicago/Turabian Style

Borges, Fabiano Tonaco, Gabriela do Manco Machado, Maíra Araújo de Santana, Karla Amorim Sancho, Giovanny Vinícius Araújo de França, Wellington Pinheiro dos Santos, and Carlos Eduardo Gomes Siqueira. 2026. "LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings" International Journal of Environmental Research and Public Health 23, no. 1: 81. https://doi.org/10.3390/ijerph23010081

APA Style

Borges, F. T., Machado, G. d. M., Santana, M. A. d., Sancho, K. A., França, G. V. A. d., Santos, W. P. d., & Siqueira, C. E. G. (2026). LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings. International Journal of Environmental Research and Public Health, 23(1), 81. https://doi.org/10.3390/ijerph23010081

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