ArticleBMC pregnancy and childbirth2025
Artificial intelligence-assisted chatbot: impact on breastfeeding outcomes and maternal anxiety.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effects of Digital Health Interventions on Breastfeeding Rates, Self-Efficacy, and Knowledge: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The Efficacy of Rule-Based Versus Large Language Model-Based Chatbots in Alleviating Symptoms of Depression and Anxiety: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial Intelligence in Family Nursing: An Evidence-Informed Policy Framework for Safe and Equitable Integration.International nursing review · 2026Review
- Experiences of Primiparous Mothers Using ChatGPTfor Breastfeeding and Infant Care.Maternal & child nutrition · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly used in healthcare interventions to provide accessible, continuous, and personalized patient support. This study investigates the impact of a mobile breastfeeding counseling application developed with artificial AI on mothers' breastfeeding self-efficacy, success, and anxiety levels.
methodsA quasi-experimental design was employed, involving 60 mothers. Participants were divided into two groups: 30 mothers received AI-based counseling, and 30 mothers were provided a booklet. Data collection tools included a personal information form, Breastfeeding Charting System and Assessment Tool (LATCH), Postnatal Breastfeeding Self-Efficacy Scale, and Beck Anxiety Inventory. Data were collected from mothers who delivered at a state hospital's obstetrics and gynecology department and were followed for ten days postpartum (postpartum days 1, 3, 7, and 10).
resultsNo significant differences were found in the demographic characteristics of the two groups (p > 0.05). Statistically significant improvements were observed in breastfeeding self-efficacy over time for both groups (AI group: f = 36.356, p = 0.000; booklet group: f = 43.349, p = 0.000). At day 10, the AI group scored significantly higher than the booklet group (Z=-2.216, p = 0.027). For breastfeeding success, as measured by the LATCH tool, significant differences were also noted over time for both groups (AI group: f = 68.466, p = 0.000; booklet group: f = 68.088, p = 0.000). At day seven, the AI group outperformed the booklet group (Z=-2.995, p = 0.003). Anxiety levels showed no significant differences between groups.
conclusionsAI-based breastfeeding counseling positively impacts breastfeeding self-efficacy and success. The findings highlight the potential of AI applications in healthcare. AI-based chatbots can serve as effective tools for breastfeeding education, offering accessible, personalized, and continuous support. The significant improvements in breastfeeding outcomes indicate that innovative AI-assisted interventions can effectively support mothers during the critical early postpartum period. This research demonstrates the feasibility of integrating AI technology into maternal care and serves as a foundation for future studies. CLINICAL TRIAL NUMBER: Not applicable.
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