ClearMed
AI in MedicineRandomized Clinical Trial5 min read

Generative Artificial Intelligence and Maternal Health Outcomes in Pregnant and Postpartum Individuals: A Randomized Clinical Trial

This 2-group randomized clinical trial enrolled 400 participants from October 2023 to December 2024, with 12-month follow-up completed in December 2025.

Published in JAMA Network OpenRandomized controlled trial + Clinical AI evaluation
5/5 Gold Standard
Evidence Meter · 5/5

Gold-Standard Human Evidence

Backed by randomized controlled human trials or a formal meta-analysis pooling multiple clinical studies.

Source

Study Duration

12-month

In this randomized clinical trial of pregnant and postpartum individuals from racial and ethnic minoritized groups, access to a generative artificial intelligence maternal health chatbot did not improve maternal anxiety or depression at 12 months compared with an active control.

What Changes?

AI in Medicine · Randomized Clinical Trial

Current Standard

Conventional ai in medicine practice relies on standard-of-care monitoring and existing regimens prior to this + clinical ai evaluation.

New Finding

Secondary and exploratory findings should be interpreted cautiously.

3 Key Takeaways

Plain English Summary
  1. 01Core Finding

    Participants were recruited online across 49 US states and the District of Columbia.

  2. 02Study Evidence

    Tested in a + clinical ai evaluation (400 participants) at the randomized clinical trial stage.

  3. 03Practical Meaning

    AI model accuracy depends on training dataset diversity and requires prospective validation across independent hospitals before routine rollout.

What This Means for You

In this randomized clinical trial of pregnant and postpartum individuals from racial and ethnic minoritized groups, access to a generative artificial intelligence maternal health chatbot did not improve maternal anxiety or depression at 12 months compared with an active control. Secondary and exploratory findings should be interpreted cautiously.

The Discovery

To evaluate whether access to a generative AI maternal health chatbot improved 12-month maternal health outcomes. This 2-group randomized clinical trial enrolled 400 participants from October 2023 to December 2024, with 12-month follow-up completed in December 2025. Participants were recruited online across 49 US states and the District of Columbia. Eligible participants were English-speaking, aged 14 years or older, pregnant or parenting an infant younger than 6 months, and self-identified as members of racial and/or ethnic minoritized groups. Primary outcomes were anxiety and depression symptoms at 12 months measured using the Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9. Secondary outcomes included emergency department visits, pregnancy-related complications, postpartum care checkups, effective contraception use, and sleep duration. Among 400 randomized participants, the mean (SD) age was 32.6 (5.1) years; 200 (50%) were parenting an infant at enrollment; and 10 (2%) identified as American Indian or Alaska Native, 80 (20%) Asian, 188 (47%) Black or African American, 57 (14%) multiracial, and 65 (16%) Hispanic or Latino. The AI chatbot was not associated with differences in anxiety scores (adjusted mean difference, 0.10; 95% CI, -0.77 to 0.96) or depression scores (adjusted mean difference, -0.23; 95% CI, -1.05 to 0.59), nor with emergency department visits, postpartum care, contraception use, or sleep duration. Among participants pregnant at enrollment, the chatbot was associated with more self-reported pregnancy-related complications in the secondary 12-month end point analysis, but not in longitudinal analyses. Higher chatbot engagement was associated with longer sleep duration and higher anxiety scores in exploratory within-group analyses.

How It Works, Simply

This ai in medicine research focuses on AI model. Researchers evaluated it using a randomized controlled trial + Clinical AI evaluation (400 participants) at the randomized clinical trial stage of medical research.

3 Key Takeaways

Clinical Takeaways
  1. 01Core Finding

    Participants were recruited online across 49 US states and the District of Columbia.

  2. 02Study Evidence

    Tested in a + clinical ai evaluation (400 participants) at the randomized clinical trial stage.

  3. 03Practical Meaning

    AI model accuracy depends on training dataset diversity and requires prospective validation across independent hospitals before routine rollout.

Key Numbers

Quantitative Study Readout
400 participants
Study Cohort
01

Biological Mechanism & Study Design

Study Design & Cohort

Randomized controlled trial + Clinical AI evaluation

Randomized controlled trial + Clinical AI evaluation (400 participants) · Randomized Clinical Trial

Biological Pathway & Modality

AI model

Intervention: Generative Artificial Intelligence and Maternal Health Outcomes in Pregnant and

02

Experimental Findings & Endpoints

To evaluate whether access to a generative AI maternal health chatbot improved 12-month maternal health outcomes.

This 2-group randomized clinical trial enrolled 400 participants from October 2023 to December 2024, with 12-month follow-up completed in December 2025. Participants were recruited online across 49 US states and the District of Columbia.

Eligible participants were English-speaking, aged 14 years or older, pregnant or parenting an infant younger than 6 months, and self-identified as members of racial and/or ethnic minoritized groups. Primary outcomes were anxiety and depression symptoms at 12 months measured using the Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9.

Secondary outcomes included emergency department visits, pregnancy-related complications, postpartum care checkups, effective contraception use, and sleep duration. Among 400 randomized participants, the mean (SD) age was 32.6 (5.1) years; 200 (50%) were parenting an infant at enrollment; and 10 (2%) identified as American Indian or Alaska Native, 80 (20%) Asian, 188 (47%) Black or African American, 57 (14%) multiracial, and 65 (16%) Hispanic or Latino.

The AI chatbot was not associated with differences in anxiety scores (adjusted mean difference, 0.10; 95% CI, -0.77 to 0.96) or depression scores (adjusted mean difference, -0.23; 95% CI, -1.05 to 0.59), nor with emergency department visits, postpartum care, contraception use, or sleep duration. Among participants pregnant at enrollment, the chatbot was associated with more self-reported pregnancy-related complications in the secondary 12-month end point analysis, but not in longitudinal analyses.

Higher chatbot engagement was associated with longer sleep duration and higher anxiety scores in exploratory within-group analyses.

03

Clinical Translation & Practice Context

In this randomized clinical trial of pregnant and postpartum individuals from racial and ethnic minoritized groups, access to a generative artificial intelligence maternal health chatbot did not improve maternal anxiety or depression at 12 months compared with an active control.

Secondary and exploratory findings should be interpreted cautiously.

03

The Bottom Line · Clinical Pearl

“In this randomized clinical trial of pregnant and postpartum individuals from racial and ethnic minoritized groups, access to a generative artificial intelligence maternal health chatbot did not improve maternal anxiety or depression at 12 months compared with an active control.”

Published in JAMA Network Open · Current Stage: Randomized Clinical Trial

AI in Medicine · Randomized Clinical Trial
Study Type
Randomized controlled trial + Clinical AI evaluation

Randomized controlled trial + Clinical AI evaluation (400 participants)

Evidence Score
5/5 · Gold-Standard Human Evidence
Evidence Meter · 5/5

Gold-Standard Human Evidence

Backed by randomized controlled human trials or a formal meta-analysis pooling multiple clinical studies.

Backed by randomized controlled human trials or a formal meta-analysis pooling multiple clinical studies.

Who It Applies To
Randomized controlled trial + Clinical AI evaluation (400 participants) in ai in medicine care
Intervention & Mechanism
Generative Artificial Intelligence and Maternal Health Outcomes in Pregnant and

AI model

Key Limitation
AI model accuracy depends on training dataset diversity and requires prospective validation across independent hospitals before routine rollout.

Translational Stage

LEVEL 1 · Randomized Clinical Trial

Classified as Randomized controlled trial + Clinical AI evaluation along the clinical evidence continuum

  1. Lab & Discovery
  2. Early Trials
  3. 3Randomized Clinical TrialActive
  4. 4Meta & Practice

Indexed Clinical Entities

Original Publication & AMA Citation

Generative Artificial Intelligence and Maternal Health Outcomes in Pregnant and Postpartum Individuals: A Randomized Clinical Trial. JAMA Network Open. Published 2026;Nov 2. Accessed November 15, 2026. https://pubmed.ncbi.nlm.nih.gov/42981095/

JAMA Network Open

https://pubmed.ncbi.nlm.nih.gov/42981095/

Read Original Paper

Continue Reading

Related in AI in Medicine

View all AI in Medicine briefings →
AI in Medicine editorial photograph for ClinicalAge, a framework for modelling ageing trajectories from longitudinal electronic health records in the UK: a model development and validation study
AI IN MEDICINE · CLINICAL PRACTICE

ClinicalAge, a framework for modelling ageing trajectories from longitudinal electronic health records in the UK: a model development and validation study

Your Medical Records Could Reveal Your True 'Biological Age' — And Predict Your Health Future. A new AI tool, ClinicalAge, uses your existing health records to calculate your biological age, offering a powerful predictor of future health risks and lifespan.

3/5 Observational
Evidence Meter · 3/5

Real-World Observation

Finds meaningful patterns in human health records or cohorts, though observational data cannot prove direct cause.

1 min readThe Lancet Digital Health
AI IN MEDICINE · CLINICAL PRACTICE

AI Enhances Bionic Vision Precision in Brain Implant Patient

An AI-powered system can precisely predict and generate personalized visual percepts in a blind individual with a brain implant by learning their unique cortical responses to electrical stimulation. Current bionic eye technologies, while offering some degree of visual perception, often struggle with precision, consistency, and the intuitive generation of meaningful visual information for the patient.

3/5 Observational
Evidence Meter · 3/5

Real-World Observation

Finds meaningful patterns in human health records or cohorts, though observational data cannot prove direct cause.

1 min readScienceDaily
AI IN MEDICINE · PRECLINICAL

AI Discovers Natural Molecule for Obesity with Promising Side Effect Profile

AI-guided discovery of the natural molecule BRP offers a novel, potentially better-tolerated approach to obesity treatment than existing medications. BRP represents a potential 'Ozempic-like' treatment for obesity that may circumvent common digestive side effects and muscle loss often associated with current GLP-1 agonists.

2/5 Preclinical
Evidence Meter · 2/5

Animal & Preclinical Data

Demonstrated in living animal models before large-scale human safety and dosing trials begin.

1 min readScienceDaily