ReviewJournal of assisted reproduction and genetics2026
Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.
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.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo review the current evidence on artificial intelligence (AI) in reproductive medicine, with particular emphasis on education, training, assessment, risks, and supervised implementation considerations.
methodsWe performed a structured literature search of studies published during the past 10 years in PubMed/MEDLINE, Web of Science Core Collection, and Scopus. We included studies that explicitly addressed education, training, assessment, curriculum, or competency development in reproductive medicine or closely related obstetrics and gynecology settings. Purely clinical prediction studies without clear educational relevance were excluded.
resultsThe literature clusters into five domains: large language models (LLMs)/generative AI, AI-assisted ultrasound training, embryology and assisted reproductive technology (ART) workflow training, AI-enabled assessment and feedback, and implementation governance. LLMs may support case discussion, formative feedback, and examination preparation, but hallucinations, unreliable citations, and overreliance remain major concerns. AI-assisted ultrasound systems may improve standard-view acquisition and measurement consistency, although performance in atypical cases remains uncertain. In embryology and ART workflows, AI may improve reproducibility and quality assurance, but current evidence does not support unsupervised replacement of expert judgment. AI-supported assessment is increasingly feasible, but high-stakes evaluation should prioritize reasoning, evidence tracing, and uncertainty management rather than fluent output alone. Across domains, successful implementation depends on faculty development, human oversight, and governance frameworks that address transparency, bias, and model monitoring.
conclusionIn reproductive medicine education, AI is best understood as a supervised adjunct rather than an autonomous substitute. The most defensible approach combines phased curricular integration, explicit guardrails, trust calibration, and stronger longitudinal evaluation of learner and workflow outcomes.
Indexed as
Identifiers
42720906What Socratic holds
Registered trials
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 Socratic graph.