Evidence map›Paper›PMID 42091849›Full record

ReviewJournal of assisted reproduction and genetics2026

Fertility care in the age of algorithms: opportunities and risks of AI and social media integration in reproductive medicine.

Tommy Wondrasek, Elizabeth Boucher, Jennifer Dundee, Jessica Ryniec, Navid Esfandiari

Abstract readReview
In one paragraph

Review in Journal of assisted reproduction and genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tommy WondrasekLarner College of Medicine, University of Vermont, Burlington, United States.
Elizabeth BoucherDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics, Midwifery, Gynecology, and Reproductive Sciences, , University of Vermont Medical Center, Burlington, United States.
Jennifer DundeeDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics, Midwifery, Gynecology, and Reproductive Sciences, , University of Vermont Medical Center, Burlington, United States.
Jessica RyniecCCRM Boston, Boston, United States.
Navid EsfandiariDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics, Midwifery, Gynecology, and Reproductive Sciences, , University of Vermont Medical Center, Burlington, United States. navid.esfandiari@uvmhealth.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Media representations have long influenced public understandings of infertility and assisted reproductive technologies (ART), with traditional media frequently relying on moralized or sensational narratives that reinforce stigma and oversimplification. The rise of social media and artificial intelligence (AI) has reshaped reproductive health communication, allowing patients to share lived experiences, build community, and access information outside clinical settings, while also increasing exposure to misinformation. This review synthesizes peer-reviewed research, clinical commentary, and digital media analyses to examine infertility narratives, online patient engagement, and AI-driven information dissemination. Findings indicate that social media has expanded patient agency and reduced isolation, but AI-generated content and influencer marketing contribute to the rapid spread of inaccurate or misleading fertility information. Despite these risks, digital platforms offer significant opportunities for evidence-based education and empathetic engagement. Clinicians who thoughtfully engage with social media and AI can counter misinformation, direct patients to trustworthy resources, and strengthen patient-physician relationships. When used responsibly, these tools can enhance communication and promote more informed, compassionate infertility care.

Indexed as

Artificial IntelligenceFertilityInfertilityReproductive MedicineReproductive Techniques, AssistedSocial MediaAlgorithmsDigital MediaFemaleHumansARTArtificial intelligenceHealth communicationInfertilitySocial media

Identifiers

PMID42091849
PMCPMC13319589

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.