ARTIFICIAL INTELLIGENCE IN BRAZILIAN PRIMARY HEALTH CARE: POTENTIALS, ETHICAL RISKS AND IMPLICATIONS FOR EQUITY IN THE SUS

Authors

DOI:

https://doi.org/10.66104/2rtma471

Keywords:

Artificial intelligence, Primary Health Care, Health equity, Unified Health System, Ethics

Abstract

The incorporation of artificial intelligence (AI) into health systems has expanded opportunities for clinical decision support, risk stratification, task automation, epidemiological surveillance and care organization. However, in Primary Health Care (PHC), these applications operate in settings marked by territorial, racial, socioeconomic and digital inequalities, which may transform efficiency gains into new forms of exclusion. This study critically analyzed the potential benefits, ethical risks and equity implications arising from the use of AI in Brazilian PHC and the Unified Health System (SUS). An integrative review covering 2016–2026 was conducted using national and international scientific literature and regulatory documents on digital health, data protection and algorithmic governance. The synthesis addressed potential applications and benefits; sources of bias and inequality; data protection, transparency and accountability; and governance requirements for adoption in the SUS. AI may support early risk identification, reduce administrative workload, enhance teams’ analytical capacity and improve care coordination. Nevertheless, incomplete data, underrepresentation of population groups, inappropriate target variables, limited explainability, technological dependence and lack of subgroup monitoring may reproduce or intensify inequities. Ethical adoption in PHC therefore requires local validation, human oversight, continuous assessment of performance and equity, social participation, data protection and institutional accountability. In the SUS, technological innovation must remain subordinate to universality, comprehensiveness and equity and cannot replace structural investment, care relationships or public governance capacity.

Downloads

Download data is not yet available.

Author Biographies

  • Ana Carolina Buchegger Marcelino Moura, Universidade Federal da Paraíba

    Graduanda em Odontologia pela Universidade Federal da Paraíba

  • Isabelly Almeida Costa, Universidade Brasil

    Acadêmica de Medicina pela Universidade Brasil

  • André Wilian Lozano, Universidade Brasil

    Doutor em Enfermagem pela Universidade Federal de São Carlos- UFSCAR

  • Valéria Albuquerque Vaz Rodrigues, Universidade Brasil

    Especialista em Educação Permanente em Saúde pela FIOCRUZ

  • Ana Paula de Lima, Universidade Brasil

    Mestranda em Engenharia Biomédica pela Universidade Brasil – UB

  • Wagner Rafael da Silva, Universidade Brasil

    Doutor em Engenharia Biomédica pela Universidade Brasil – UB

  • Vanessa Dias de Oliveira Justi, Universidade Brasil

    Doutoranda em Engenharia Biomédica pela Universidade Brasil – UB

  • Alessandra Cristiane Alves do Nascimento , UNISALESIANO

    Enfermeira pelo UNISALESIANO

  • Patrícia Michelassi Carrinho Aureliano, Universidade Brasil

    Doutora em Engenharia Biomédica pela Universidade Brasil

  • José Martins Pinto Neto, Universidade Brasil

    Doutor em Ciências pela Universidade de São Paulo – USP

  • Nicezia Vilela Junqueira Franqueiro, Universidade Brasil

    Doutora em Engenharia Biomédica pela Universidade Brasil

  • Bianca Ortunho Boato, Instituto Albert Einstein

    Especialista em Gestão e Acreditação com Ênfase na Metodologia ONA pelo Instituto Albert Einstein

  • Valter Mariano dos Santos Junior, Universidade Brasil

    Doutor em Ciências pela Universidade Federal de São Carlos – UFSCar

References

BERDAHL, C. T. et al. Strategies to improve the impact of artificial intelligence on health equity: scoping review. JMIR AI, Toronto, v. 3, e52936, 2024. DOI: 10.2196/52936.

BRASIL. Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da União: seção 1, Brasília, DF, 15 ago. 2018.

BRASIL. Ministério da Saúde. Estratégia de Saúde Digital para o Brasil 2020–2028. Brasília, DF: Ministério da Saúde, 2020.

BRASIL. Ministério da Saúde. Portaria GM/MS nº 1.768, de 30 de julho de 2021. Altera o Anexo XLII da Portaria de Consolidação GM/MS nº 2, de 28 de setembro de 2017, para dispor sobre a Política Nacional de Informação e Informática em Saúde. Diário Oficial da União: seção 1, Brasília, DF, 2 ago. 2021a.

BRASIL. Conselho Nacional de Saúde. Resolução nº 659, de 26 de julho de 2021. Dispõe sobre a Política Nacional de Informação e Informática em Saúde. Brasília, DF: Conselho Nacional de Saúde, 2021b.

CHEN, I. Y.; PIERSON, E.; ROSE, S.; JOSHI, S.; FERRyman, K.; GHasSEMI, M. Ethical machine learning in healthcare. Annual Review of Biomedical Data Science, Palo Alto, v. 4, p. 123–144, 2021. DOI: 10.1146/annurev-biodatasci-092820-114757.

CHEN, R. J. et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, London, v. 7, p. 719–742, 2023. DOI: 10.1038/s41551-023-01056-8.

CHIN, M. H. et al. Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care. JAMA Network Open, Chicago, v. 6, n. 12, e2345050, 2023. DOI: 10.1001/jamanetworkopen.2023.45050.

COOTS, M.; LINN, K. A.; GOEL, S.; et al. Racial bias in clinical and population health algorithms: a critical review of current debates. Annual Review of Public Health, Palo Alto, v. 45, p. 173–192, 2024. DOI: 10.1146/annurev-publhealth-071823-112058.

GAO, Q. et al. Opportunities and challenges of artificial intelligence in public health: a systematic review and research agenda. Frontiers in Public Health, Lausanne, v. 14, 2026.

LIN, S. Y. A clinician’s guide to artificial intelligence: why and how primary care should lead the health care AI revolution. Journal of the American Board of Family Medicine, Lexington, v. 35, n. 1, p. 175–184, 2022. DOI: 10.3122/jabfm.2022.01.210226.

MARTÍNEZ-MARTÍNEZ, H. et al. Perceptions of, barriers to, and facilitators of the use of artificial intelligence in primary care: qualitative study with professionals and patients. Journal of Medical Internet Research, Toronto, v. 27, 2025.

MORLEY, J. et al. The ethics of AI in health care: a mapping review. Social Science & Medicine, Oxford, v. 260, 113172, 2020. DOI: 10.1016/j.socscimed.2020.113172.

OBERMEYER, Z.; POWERS, B.; VOGELI, C.; MULLAINATHAN, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science, Washington, DC, v. 366, n. 6464, p. 447–453, 2019. DOI: 10.1126/science.aax2342.

RAJKOMAR, A.; HARDT, M.; HOWELL, M. D.; CORRADO, G.; CHIN, M. H. Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, Philadelphia, v. 169, n. 12, p. 866–872, 2018. DOI: 10.7326/M18-1990.

SASSEVILLE, M. et al. Risk of bias mitigation for vulnerable and diverse groups in community-based primary health care artificial intelligence models: a scoping review. Journal of Medical Internet Research, Toronto, v. 25, e46679, 2023. DOI: 10.2196/46679.

SENDAK, M.; BALU, S.; HERNANDEZ, A. F. Proactive algorithm monitoring to ensure health equity. JAMA Network Open, Chicago, v. 6, n. 12, e2345022, 2023. DOI: 10.1001/jamanetworkopen.2023.45022.

WANG, J. X. et al. Health equity in artificial intelligence and primary care research: scoping review. Journal of Medical Internet Research, Toronto, v. 23, n. 9, e27799, 2021. DOI: 10.2196/27799.

WORLD HEALTH ORGANIZATION. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: WHO, 2021.

WORLD HEALTH ORGANIZATION. Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. Geneva: WHO, 2025.

Published

2026-06-15

How to Cite

ARTIFICIAL INTELLIGENCE IN BRAZILIAN PRIMARY HEALTH CARE: POTENTIALS, ETHICAL RISKS AND IMPLICATIONS FOR EQUITY IN THE SUS. (2026). REMUNOM, 13(13), 1-19. https://doi.org/10.66104/2rtma471