Artificial intelligence for sexual, reproductive and maternal health in Latin America and the Caribbean: a scoping review.

Authors

Martin Saban, Melina Denise Zavala, Adolfo Rubinstein, Santiago Esteban, Cintia Cejas

Published in

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology. Volume 46. Issue 1. Pages 2691044. Epub Jul 05, 2026.

Abstract

Artificial intelligence (AI) holds considerable promise for strengthening sexual, reproductive and maternal health (SRMH) by enhancing diagnosis, optimising service delivery and expanding access to information. In Latin America and the Caribbean (LAC), however, the scope, focus and maturity of AI applications in SRMH remain poorly described.
To identify, map and analyse existing applications of AI in SRMH priority services in LAC, characterising thematic areas, target populations, and types of AI tools, whilst highlighting gaps and future research needs.
We conducted a scoping review guided by the Arksey and O'Malley framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Searches were performed in PubMed, SciELO, Cochrane and LILACS up to August 2023, complemented by targeted Google searches and snowballing. We included records reporting AI applications in SRMH services in LAC and extracted data on setting, population, SRMH domain, AI techniques and implementation stage.
A total of 1,518 records were identified, of which 143 met the inclusion criteria. Most were published between 2020 and 2023 and originated from Mexico, Colombia, Peru, Brazil and Argentina. Over half were peer-reviewed articles, with additional theses and web-based reports. Applications concentrated on prenatal, childbirth and postnatal care (36%) and reproductive organ cancers (31%), with far fewer initiatives addressing sexual health, contraception, gender-based violence, sexual satisfaction or counselling. Machine learning methods predominated (52%), followed by deep learning (41%). Almost half of initiatives (48%) were exploratory projects, 17% implemented tools without outcome data and 35% reported performance in real-world contexts.
AI applications in SRMH in LAC are expanding but remain thematically narrow, population-selective and predominantly exploratory, highlighting the need for more diverse, rigorously evaluated and equity-oriented tools.

PMID:
42402197
Bibliographic data and abstract were imported from PubMed on 06 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.