ReviewHuman reproduction (Oxford, England)2024
A brief history of artificial intelligence embryo selection: from black-box to glass-box.
Review in Human reproduction (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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.
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Who cites it
24 citing papers in PubMed.
- Embryo selection using artificial intelligence: moving from validation to responsible clinical integration.Journal of assisted reproduction and genetics · 2026Article
- Addressing multiple pregnancies in in vitro fertilization-embryo transfer: Current status, challenges, and solutions.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Review
- Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation.Journal of clinical medicine · 2026Review
- Contemporary Approaches Towards the Optimization of Embryo Implantation.Journal of clinical medicine · 2026Review
- Alignment among the zygotic cleavage plane, pronuclear axis, and polar axis predicts live birth outcome of blastocyst.Journal of assisted reproduction and genetics · 2026Article
- Explainable Artificial Intelligence in Assisted Reproductive Technology: Bridging Prediction and Clinical Judgment.Biomedicines · 2026Review
- The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).Reproductive biology and endocrinology : RB&E · 2026Review
- Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning.Scientific reports · 2026Article
- Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.Frontiers in reproductive health · 2026Article
- Correlation of fetal heartbeat outcome after Day 3 or Day 5 single embryo transfer of morphologically selected embryos with an annotation-free deep learning scoring system: Results from a multi-center study.Journal of assisted reproduction and genetics · 2025Observational
- Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.Scientific reports · 2025Article
- Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.European journal of medical research · 2025Article
- A foundational model for in vitro fertilization trained on 18 million time-lapse images.Nature communications · 2025Article
- Article
- Development and validation of machine learning models for predicting blastocyst yield in IVF cycles.Scientific reports · 2025Article
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- Deep Learning Classification of Rheumatoid Arthritis in Hand Radiographs Interpretability Insights and Web Application.ImmunoTargets and therapy · 2025Article
- Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency.Biology · 2024Review
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.
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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.