ReviewJournal of assisted reproduction and genetics2026
AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation.
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
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
Infertility represents a growing global health challenge, intensifying the demand for advanced assisted reproductive technology (ART). Artificial intelligence (AI) is emerging as a transformative force in reproductive medicine, offering novel solutions to augment clinical success and optimize patient-centered care. This review comprehensively synthesizes AI advancements across the continuum of ART, including sperm and oocyte evaluation, embryo selection, pregnancy prediction, fertility assessment, and supportive nursing. Through the integration of multimodal data, extraction of discriminative features, and construction of predictive models, AI introduces unprecedented objectivity and precision into gamete and embryo analysis, thereby facilitating personalized treatment strategies. Furthermore, intelligent consultation and management systems powered by large language models are redefining reproductive healthcare delivery by enhancing clinician-patient communication and improving engagement. While challenges pertaining to data privacy and model generalizability remain, the deep integration of AI with reproductive medicine is an irreversible trend poised to overcome existing ART bottlenecks and forge a more efficient, humane diagnostic and therapeutic ecosystem.
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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 OpenQuestion graph.