ReviewHuman reproduction (Oxford, England)2025
Ethics of artificial intelligence in embryo assessment: mapping the terrain.
Review in Human reproduction (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.Journal of assisted reproduction and genetics · 2026Review
- Two sides of the same algorithm: a qualitative study of ART professionals' and patients' views on the use of machine learning for embryo assessment and selection.Journal of assisted reproduction and genetics · 2026Article
- Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications.Bioengineering (Basel, Switzerland) · 2026Review
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Clinical evaluation and predictive model development of AI-based non-invasive embryo selection inFrontiers in endocrinology · 2026Article
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Genetic and Epigenetic Risks of Male Infertility in ART.International journal of molecular sciences · 2025Review
- The ART of Embryo Selection: A Review of Methods to Rank the Most Competent Embryo(s) for Transfer to Optimize IVF Success.Biomedicines · 2025Review
- Private equity and reproductive medicine: "Fertile breeding ground" - a physician's perspective".Reproductive biology and endocrinology : RB&E · 2025Review
- Deep learning classification integrating embryo images with associated clinical information from ART cycles.Scientific reports · 2025Article
- Artificial Intelligence in Assisted Reproductive Technology: A New Era in Fertility Treatment.Cureus · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Artificial intelligence (AI) has the potential to standardize and automate important aspects of fertility treatment, improving clinical outcomes. One promising application of AI in the fertility clinic is the use of machine learning (ML) tools to assess embryos for transfer. The successful clinical implementation of these tools in ways that do not erode consumer trust requires an awareness of the ethical issues that these technologies raise, and the development of strategies to manage any ethical concerns. However, to date, there has been little published literature on the ethics of using ML in embryo assessment. This mini-review contributes to this nascent area of discussion by surveying the key ethical concerns raised by ML technologies in healthcare and medicine more generally, and identifying which are germane to the use of ML in the assessment of embryos. We report concerns about the 'dehumanization' of human reproduction, algorithmic bias, responsibility, transparency and explainability, deskilling, and justice.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.