Article

What it takes to implement AI in Africa: health-system lessons from developing an ML-enabled maternal risk stratification algorithm in Tanzania

2026-07-28

Abstract excerpt

<h4>Background: </h4> Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. However, most studies focus on model development and predictive performance, with less attention to the health-system processes required to generate ML-ready data and translate risk information into clinical action. The Mlinde Mama Project in Tanzania combined...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 64a7caf3-c49b-56a6-b13c-b78a5595d7fa
DOI: 10.64898/2026.07.26.26358974

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

What it takes to implement AI in Africa: health-system lessons from developing an ML-enabled maternal risk stratification algorithm in TanzaniaDOI 10.64898/2026.07.26.26358974Read

An Uneven Century: Navigating Health Disparities in Maternal and Neonatal Care in the MENA Region Through Artificial IntelligenceSemantic proximity Read

Human-supervised, large language model-based clinical decision support aligned to national newborn protocols in Kenya: a pragmatic, early-stage evaluationSemantic proximity Read

Predictive Artificial Intelligence Models for Non-Communicable Disease Burden Forecasting in AfricaSemantic proximity Read

Feasibility, acceptance, and workflow integration of an AI-enabled clinical decision support system for non-communicable diseases in Kiambu County, Kenya: A mixed-methods implementation evaluationSemantic proximity Read

ARTIFICIAL INTELLIGENCE IN AFRICAN CARDIOVASCULAR CARE: OPPORTUNITIES, CHALLENGES, AND PATHWAYS TO IMPROVED OUTCOMESSemantic proximity Read

Leveraging Foundation Models in Maternal and Child Health: A Systematic ReviewSemantic proximity Read

AI decision-making performance in Maternal-Fetal Medicine compared with human specialists: a cross-sectional studySemantic proximity Read

Before You Build: Seven Misconceptions Health AI Developers Must Confront in Global Public HealthSemantic proximity Read

Artificial Intelligence in Medical Diagnosis and Treatment Planning Among Healthcare Professionals in a Tertiary Hospital, in TanzaniaSemantic proximity Read

Health Managers’ Perspectives on Utilization and Barriers of Health Apps in Indonesian Maternal Health Services: A Qualitative Descriptive StudySemantic proximity Read

Application of Machine Learning in Hypertension Research and Management in Nigeria: A Systematic ReviewSemantic proximity Read

Development of an LLM Pipeline Surpassing Physicians in Cardiovascular Risk Score CalculationSemantic proximity Read