ArticleJournal of assisted reproduction and genetics2021
Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.
Article in Journal of assisted reproduction and genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 32 citations in OpenAlex.
- Current state of artificial intelligence applications in assisted reproductive technology: A narrative review.Clinical and experimental reproductive medicine · 2026Article
- Fertility care in the age of algorithms: opportunities and risks of AI and social media integration in reproductive medicine.Journal of assisted reproduction and genetics · 2026Review
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Conceptualizing clinical expertise in evidence-based practice: a narrative literature review with implications for clinical decision-making.The Journal of the Canadian Chiropractic Association · 2025Article
- Assisted Reproductive Technology: A Ray of Hope for Infertility.ACS omega · 2025Review
- Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.Reproductive biology and endocrinology : RB&E · 2025Article
- AI for One Welfare: the role of animal welfare scientists in developing valid and ethical AI-based welfare assessment tools.Frontiers in veterinary science · 2025Article
- GYNs at the REI gates: unsolvable conundrum or unambiguous opportunity?Journal of assisted reproduction and genetics · 2024Article
- Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.Journal of medical Internet research · 2024Article
- The 10 "Cardinal Sins" in the Clinical Diagnosis and Treatment of Endometriosis: A Bayesian Approach.Journal of clinical medicine · 2023Article
- At-home urine estrone-3-glucuronide quantification predicts oocyte retrieval outcomes comparably with serum estradiol.F&S reports · 2023Article
- Proceedings of the first world conference on AI in fertility.Journal of assisted reproduction and genetics · 2023Article
- Reconsidering evidence-based management of endometriosis.Facts, views & vision in ObGyn · 2022Article
- Artificial intelligence-the future is now.Journal of assisted reproduction and genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decision-making in fertility care is on the cusp of a significant frameshift. Online tools to integrate artificial intelligence into the decision-making process across all aspects of ART are rapidly emerging. These tools have the potential to improve outcomes and transition decision-making from one based on traditional provider centric assessments toward a hybrid triad of expertise, evidence, and algorithmic data analytics using AI. We can look forward to a time when AI will be the third part of a provider's tool box to complement expertise and medical literature to enable ever more accurate predictions and outcomes in ART. In their fully integrated format, these tools will be part of a digital fertility ecosystem of analytics embedded within an EMR. To date, the impact of AI on ART outcomes is inconclusive. No prospective studies have shown clear cut benefit or cost reductions over current practices, but we are very early in the process of developing and evaluating these tools. We owe it to ourselves to begin to examine these AI-driven analytics and develop a very clear idea about where we can and should go before we roll these tools into clinical care. Thoughtful scrutiny is essential lest we find ourselves in a position of trying to modulate and modify after entry of these tools into our clinics and patient care. The purpose of this commentary is to highlight the evolution and impact AI has had in other fields relevant to the fertility sector and describe a vision for applications within ART that could improve outcomes, reduce costs, and positively impact clinical care.
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.