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Currently submitted to: Journal of Medical Internet Research

Date Submitted: Jan 27, 2026
Open Peer Review Period: Jan 27, 2026 - Mar 24, 2026
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The Impact of AI-driven tools on Breastfeeding Outcomes: Systematic Review and Meta-Analysis

  • Jiahe Sun; 
  • Yu Wang; 
  • Shuang Hu; 
  • Yajie Ding; 
  • Congshan Pu; 
  • Danni Song; 
  • Jia’ai Xia; 
  • Chunjian Shan

ABSTRACT

Background:

The current global breastfeeding landscape presents both progress and challenges. The rise of artificial intelligence (AI) has emerged as a promising new strategy to enhance breastfeeding practices.

Objective:

To evaluate the impact of AI-driven tools on breastfeeding practices and outcomes.

Methods:

We searched PubMed, Web of Science, Cochrane Library, Embase, and CINAHL from inception to October 2025 for randomized controlled trials (RCTs) and quasi-experimental studies. The risk of bias in individual studies was assessed using the Cochrane risk of bias tool for randomized controlled trials (RoB 2) and the risk of bias in non-randomized studies of interventions tool (ROBINS-I). Data were extracted independently by two reviewers and combined using Review Manager 5.4 and R-4.5.2 to obtain pooled results via random-effects models, with subgroup analyses based on intervention type, timing of implementation, population characteristics, and country income level.

Results:

This review included 39 studies with 10735 participants from 15 countries. AI-driven tools increased exclusive breastfeeding (EBF) rates (at <3 months: relative risk [RR] 1.21, 95% CI 1.13-1.29; P<.001, I²=56%; at 3–6 months: RR 1.54; 95% CI 1.29-1.85; P<.001, I2=69%; at ≥6 months: RR 1.47, 95% CI 1.22-1.77, P<.001, I2=78%), breastfeeding self-efficacy (BSE) (standardized mean difference [SMD] 0.41, 95% CI: 0.04-0.78; P=.03, I2=93%), and breastfeeding knowledge (SMD 1.69; 95% CI: 0.54-2.84, P=.004, I2=98%).

Conclusions:

AI-driven tools effectively increase exclusive breastfeeding rates, breastfeeding self-efficacy, and breastfeeding knowledge. Future studies are needed to provide stronger evidence about clinical care interventions. Clinical Trial: PROSPERO CRD420251233352; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251233352


 Citation

Please cite as:

Sun J, Wang Y, Hu S, Ding Y, Pu C, Song D, Xia J, Shan C

The Impact of AI-driven tools on Breastfeeding Outcomes: Systematic Review and Meta-Analysis

JMIR Preprints. 27/01/2026:92184

DOI: 10.2196/preprints.92184

URL: https://preprints.jmir.org/preprint/92184

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