SynthesisJournal of medical systems2023
AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.
Synthesis in Journal of medical systems, 2023. 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
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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
12 citing papers in PubMed, 25 citations in OpenAlex.
- Environmental determinants of male infertility: emerging threats and technological interventions.Frontiers in medicine · 2026Review
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.Annals of medicine · 2025Article
- Enhancing the precision of male fertility diagnostics through bio inspired optimization techniques.Scientific reports · 2025Article
- A Novel Machine Learning Model for Predicting Natural Conception Using Non-Laboratory-Based Data.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.Scientific reports · 2025Article
- Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- TCKAN: a novel integrated network model for predicting mortality risk in sepsis patients.Medical & biological engineering & computing · 2025Article
- Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases.Medical & biological engineering & computing · 2024Article
- The prediction of semen quality based on lifestyle behaviours by the machine learning based models.Reproductive biology and endocrinology : RB&E · 2024Article
- Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature.Healthcare (Basel, Switzerland) · 2024Review
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes.
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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.