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Innovations in Infertility Treatment and Assisted Reproductive Technologies (ARTs)
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Innovations in Infertility Treatment and Assisted Reproductive Technologies (ARTs)

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Obstetrics & Gynecology".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 818

Editor

Dr. Diana Alecsandru

E-Mail Website
Guest Editor
IVIRMA Global Research Alliance, IVI Madrid, Madrid, Spain
Interests: reproductive disorders; immunology; IVF clinical; female fertility; pre-implantation genetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infertility affects millions of individuals and couples on a global scale, posing not only medical challenges but also significant emotional and psychological burdens. Recent advances in infertility treatment and assisted reproductive technologies (ARTs) have revolutionized the field, offering hope and improved outcomes for patients. Thus, the aim of this Special Issue is to highlight cutting-edge innovations in diagnostic methods, treatment protocols, and laboratory techniques that are shaping the future of reproductive medicine. Topics of interest include, but are not limited to, novel ovarian stimulation protocols, advancements in in vitro fertilization (IVF), cryopreservation, preimplantation genetic testing, and emerging technologies such as artificial intelligence in embryo selection. Additionally, we seek to explore the ethical, social, and regulatory dimensions of these innovations. By bringing together original research, clinical studies, and expert reviews, this Special Issue will provide a comprehensive overview of the latest developments in the field, with the goal of enhancing clinical practice and improving reproductive outcomes for patients across diverse populations.

Dr. Diana Alecsandru
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • infertility
  • assisted reproductive technology (ARTs)
  • fertility preservation
  • in vitro fertilization (IVF)
  • reproductive endocrinology

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Published Papers (1 paper)

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Review

17 pages, 321 KB  
Review
Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation
by Daichi Inoue
J. Clin. Med. 2026, 15(13), 5157; https://doi.org/10.3390/jcm15135157 - 2 Jul 2026
Viewed by 166
Abstract
Miscarriage is a common adverse reproductive outcome, and recurrent pregnancy loss (RPL) remains a major challenge in reproductive medicine. Despite advances in genetics, immunology, endocrinology, and endometrial biology, many RPL cases remain unexplained. Conventional statistical approaches may be limited in capturing high-order nonlinear [...] Read more.
Miscarriage is a common adverse reproductive outcome, and recurrent pregnancy loss (RPL) remains a major challenge in reproductive medicine. Despite advances in genetics, immunology, endocrinology, and endometrial biology, many RPL cases remain unexplained. Conventional statistical approaches may be limited in capturing high-order nonlinear interactions among clinical, imaging, immunological, and molecular factors associated with pregnancy loss unless these interactions are explicitly modeled. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has therefore been investigated as a potential framework for reproductive risk prediction and patient stratification. This narrative review summarizes current evidence on AI-based prediction of miscarriage and RPL, with emphasis on its possible translational relevance to infertility treatment and assisted reproductive technology (ART). Clinical data-driven models have shown potentially useful discriminatory performance, while biomarker-integrated ML approaches suggest that immune-inflammatory signatures may contribute to risk estimation. Imaging-based AI, including radiomics from multimodal ultrasound, may also support noninvasive assessment of endometrial receptivity and inform embryo transfer planning. In parallel, the broader ART literature suggests increasing interest in AI for embryo selection, embryology laboratory workflow, and ovarian stimulation prediction. However, the evidence remains limited by retrospective study designs, small datasets, inconsistent RPL definitions, inadequate external validation, and concerns regarding interpretability, fairness, and regulation. Further progress will require multimodal, explainable, and prospectively validated systems linked to clinically meaningful outcomes. AI may ultimately support more individualized reproductive care, but routine clinical implementation remains premature. Full article
(This article belongs to the Special Issue Innovations in Infertility Treatment and Assisted Reproductive Technologies (ARTs))
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