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Review

Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review

by
Gustavo Gonçalves dos Santos
1,2,*,
Anuli Njoku
3,4,
Ana Carolina Pereira Mass
5,
Ellen Eduarda Santos Ribeiro
6,
Letícia Eduarda de Oliveira
7,
Maria Julia Cunha Silva Lima
8,
Taís de Abreu Ferro
9,10,
Lilian Reinaldi Ribeiro Pirozi
11,
Antônio Augusto de Freitas Peregrino
10,11,
Célia Maria Pinheiro dos Santos
12,
Lucia Helena Ferreira Viana
13,
Marilda Gonçalves de Sousa
14,
Carla Helena Cappello
15,
Cely de Oliveira
16,
Luis Henrique de Andrade
17,
Cindy Ferreira Lima
18 and
Isabelle Cristinne Pinto Costa
2
1
Programa de Pós-Graduação em Enfermagem (PPGENF/UnG), Universidade Guarulhos, Guarulhos 07023-070, SP, Brazil
2
Escola de Enfermagem, Programa Nacional de Pós-Doutorado, Programa de Pós-Graduação em Enfermagem, Universidade Federal de Alfenas (UNIFAL-MG), Alfenas 37130-001, MG, Brazil
3
Department of Public Health, College of Health and Human Services, Southern Connecticut State University (SCSU), New Haven, CT 06515, USA
4
College of Health Sciences and Public Policy, Walden University, Minneapolis, MN 55401, USA
5
Faculdade de Medicina de Marília, Residência Integrada Multiprofissional em Saúde (FAMEMA), Marília 17519-470, SP, Brazil
6
Centro de Ciências da Saúde, Departamento de Enfermagem, Programa de Pós-Graduação em Enfermagem, Universidade Federal do Piauí (UFPI), Teresina 64049-550, PI, Brazil
7
Pontifícia Universidade Católica de Minas Gerais (PUC/MG), Poços de Caldas 37714-620, MG, Brazil
8
Faculdade de Ciências Médicas da Santa Casa de São Paulo (FCMSCSP), São Paulo 01221-020, SP, Brazil
9
Departamento Materno-Infantil e Psiquiátrica, Programa de Pós-Graduação em Enfermagem, Escola de Enfermagem da Universidade de São Paulo, (EEUSP), São Paulo 05403-000, SP, Brazil
10
Escola de Enfermagem Alfredo Pinto, Programa de Pós-Graduação em Enfermagem e Biociências, Universidade Federal do Estado do Rio de Janeiro, (EEAP/PPGENFBIO/UNIRIO), Rio de Janeiro 22290-180, RJ, Brazil
11
Departamento de Ciências Radiológicas, Universidade do Estado do Rio de Janeiro (DCR/UERJ), Rio de Janeiro 20550-900, RJ, Brazil
12
Faculdade de Medicina de Botucatu, Programa de Pós-Graduação em Saúde Coletiva, Universidade Estadual Paulista “Júlio de Mesquita Filho” (FMB/UNESP), Botucatu 18618-687, SP, Brazil
13
Centro Universitário Piaget (UNIPIAGET), Suzano 08673-270, SP, Brazil
14
Centro Universitário das Faculdades Metropolitanas Unidas (FMU), São Paulo 01503-001, SP, Brazil
15
Universidade de Ribeirão Preto (UNAERP), Guarujá 11440-003, SP, Brazil
16
Universidade Santa Cecília (UNISANTA), Santos 11045-907, SP, Brazil
17
Centro Universitário Estácio, Santo André 09015-070, SP, Brazil
18
Organização Pan-Americana da Saúde (OPAS), Secretaria do Estado de Saúde de Minas Gerais, Minas Gerais 31630903, MG, Brazil
 
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*
Author to whom correspondence should be addressed.
Epidemiologia 2026, 7(3), 81; https://doi.org/10.3390/epidemiologia7030081
Submission received: 3 December 2025 / Revised: 16 March 2026 / Accepted: 14 April 2026 / Published: 10 June 2026
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Abstract

Background: Maternal mortality remains a major global health challenge, disproportionately affecting black and Indigenous women. Hypertensive disorders of pregnancy and postpartum hemorrhage are the leading direct causes of maternal death. Artificial intelligence (AI) tools have emerged as potential strategies for predicting these complications, yet concerns persist about their equity and validation across racial groups. Methods: A rapid review was conducted in five databases, PubMed, EMBASE, Web of Science, Scopus and LILACS, to synthesize recent evidence on the use of AI for preventing maternal mortality due to hypertension and postpartum hemorrhage. Studies published in the last five years that included racial or ethnic data were selected and analyzed narratively. Results: Ten studies met the inclusion criteria, showing high predictive accuracy of AI models (AUROC often >0.95) for severe maternal outcomes. However, few models incorporated racial variables or underwent external validation in racially diverse or low-resource populations. Evidence suggests that unrepresentative datasets may perpetuate or exacerbate existing health inequities. Conclusions: AI demonstrates strong technical performance in predicting maternal complications but limited equity in application. Broader racial representation, external validation, and ethical governance are essential for ensuring that AI-based tools reduce rather than reinforce racial disparities in maternal mortality.
Keywords: artificial intelligence; hypertension; postpartum hemorrhage; maternal mortality; racial disparities in health artificial intelligence; hypertension; postpartum hemorrhage; maternal mortality; racial disparities in health

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MDPI and ACS Style

Gonçalves dos Santos, G.; Njoku, A.; Mass, A.C.P.; Ribeiro, E.E.S.; Oliveira, L.E.d.; Lima, M.J.C.S.; Ferro, T.d.A.; Pirozi, L.R.R.; Peregrino, A.A.d.F.; Santos, C.M.P.d.; et al. Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review. Epidemiologia 2026, 7, 81. https://doi.org/10.3390/epidemiologia7030081

AMA Style

Gonçalves dos Santos G, Njoku A, Mass ACP, Ribeiro EES, Oliveira LEd, Lima MJCS, Ferro TdA, Pirozi LRR, Peregrino AAdF, Santos CMPd, et al. Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review. Epidemiologia. 2026; 7(3):81. https://doi.org/10.3390/epidemiologia7030081

Chicago/Turabian Style

Gonçalves dos Santos, Gustavo, Anuli Njoku, Ana Carolina Pereira Mass, Ellen Eduarda Santos Ribeiro, Letícia Eduarda de Oliveira, Maria Julia Cunha Silva Lima, Taís de Abreu Ferro, Lilian Reinaldi Ribeiro Pirozi, Antônio Augusto de Freitas Peregrino, Célia Maria Pinheiro dos Santos, and et al. 2026. "Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review" Epidemiologia 7, no. 3: 81. https://doi.org/10.3390/epidemiologia7030081

APA Style

Gonçalves dos Santos, G., Njoku, A., Mass, A. C. P., Ribeiro, E. E. S., Oliveira, L. E. d., Lima, M. J. C. S., Ferro, T. d. A., Pirozi, L. R. R., Peregrino, A. A. d. F., Santos, C. M. P. d., Viana, L. H. F., Sousa, M. G. d., Cappello, C. H., Oliveira, C. d., Andrade, L. H. d., Lima, C. F., & Costa, I. C. P. (2026). Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review. Epidemiologia, 7(3), 81. https://doi.org/10.3390/epidemiologia7030081

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