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Reprod. Med.
Special Issues
Game-Changing Concepts in Reproductive Health
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Game-Changing Concepts in Reproductive Health

A special issue of Reproductive Medicine (ISSN 2673-3897).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3239

Special Issue Editors

Prof. Dr. Mark I. Evans

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Guest Editor
Lancaster Maternal Fetal Medicine, Lancaster PA and School of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
Interests: CVS; fetal reduction; prenatal diagnosis; amniocentesis; ultrasound; nuchal translucency screening; fetal therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Christian R. Macedonia

E-Mail Website
Guest Editor
Lancaster Maternal Fetal Medicine, Lancaster PA and School of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
Interests: obstetrics; gynecology; maternal and fetal medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The published research juggernaut is largely occupied by relatively incremental studies building upon an existing foundation which flood the literature and only advance the field in small increments. Such studies often predominate for several years. Periodically, however, there are game-changing discoveries that advance our understanding onto a new playing field, often with new rules, and which force everyone to think differently to how they did the day before. It is these sentinel discoveries that make careers and can have highly leveraged implications for both individual patients and public health. In women’s reproductive medicine, the logarithmic growth of molecular techniques and accompanying bioinformatics has translated into rapid changes in our appreciation of fetal, neonatal, childhood, and adult diagnoses and set the stage for new therapies. This Special Issue seeks research articles and reviews exploring such game-changing discoveries and how they are presented, argued, resisted, and ultimately translated and implemented into actual patient care. 

You may choose our Joint Special Issue in Diagnostics.

Prof. Dr. Mark I. Evans
Dr. Christian R. Macedonia
Guest Editors

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Reproductive Medicine is an international peer-reviewed open access quarterly 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 1200 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

  • pregnancy
  • artificial intelligence
  • prenatal diagnosis
  • genetic sequencing
  • diagnostic tests
  • screening tests
  • public policy
  • electronic fetal monitoring
  • introducing new technologies

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Review

17 pages, 912 KB  
Review
Beyond Incremental: Embracing Transformative Innovation in Women’s Health
by Mark I. Evans, Lawrence D. Devoe, Gregory F. Ryan, David W. Britt and Christian R. Macedonia
Reprod. Med. 2026, 7(1), 16; https://doi.org/10.3390/reprodmed7010016 - 23 Mar 2026
Viewed by 977
Abstract
Background/Objectives: Women’s health has historically lagged behind other medical specialties in transformative innovation, despite significant technological advances in adjacent fields. In this collection of papers, we examine the current state of innovation in women’s health and maternal–fetal medicine, identify barriers to transformation, and [...] Read more.
Background/Objectives: Women’s health has historically lagged behind other medical specialties in transformative innovation, despite significant technological advances in adjacent fields. In this collection of papers, we examine the current state of innovation in women’s health and maternal–fetal medicine, identify barriers to transformation, and propose strategies for accelerating breakthrough developments. This paper presents an overview of multiple forces and their often-competing relationships that influence the environment in which advances in multiple areas of healthcare have had to navigate to enter mainstream practice. An understanding of these forces is essential to explain why some new technologies are readily deployed into clinical practice while others take many years to be adopted. Understanding the entire “echo-system” around any specific technology provides a much fuller understanding of how any individual advance can make its way into actual utilization. Methods: We synthesized current literature on innovation in women’s health, analyzing technological advances in artificial intelligence, precision medicine, non-invasive diagnostics, and surgical robotics. We examined patterns of innovation adoption and barriers to implementation across multiple domains. Results: Several key areas presented in this paper and the following show promise for transformative change: artificial intelligence (AI)-driven diagnostics achieving expert-level performance in prenatal screening, precision medicine approaches transforming genetic disease management, and non-invasive monitoring technologies revolutionizing maternal–fetal care. However, systemic barriers including regulatory complexity, liability concerns, and institutional inertia continue to limit widespread adoption of numerous breakthrough technologies. Conclusions: The convergence of multiple technological advances, particularly artificial intelligence and precision medicine, positions women’s health for unprecedented transformation. Success requires fostering innovation-ready environments, embracing systems-awareness approaches, and maintaining focus on human-centered care while leveraging technological capabilities with continual feedback and course corrections. Full article
(This article belongs to the Special Issue Game-Changing Concepts in Reproductive Health)
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27 pages, 973 KB  
Review
Early to Mature, Early to Detect: Artificial Intelligence in the Risk Prediction and Diagnosis of Precocious Puberty
by Manisha Chavan, Sameena Tabassum, Divya Dinesh Joshi, Kusalik Boppana, Nasreen Banu, Riya Kayarkar, Kalp Chauhan, Gayathri Yerrapragada, Poonguzhali Elangovan, Mohammed Naveed Shariff, Thangeswaran Natarajan, Jayarajasekaran Janarthanan, Shreshta Agarwal, Sancia Mary Jerold Wilson, Mini Virmani, Atishya Ghosh, Mimi Adu Serwaah, Shiva Sankari Karuppiah, Keerthy Gopalakrishnan, Divyanshi Sood, Swetha Rapolu, Swathi Priya Cherukuri and Shivaram P. Arunachalamadd Show full author list remove Hide full author list
Reprod. Med. 2026, 7(1), 9; https://doi.org/10.3390/reprodmed7010009 - 13 Feb 2026
Cited by 1 | Viewed by 1874
Abstract
Background/Objectives: Precocious puberty (PP), defined as the onset of secondary sexual characteristics before 8 years in girls and 9 years in boys, is associated with psychosocial distress, compromised adult height, and long-term metabolic risk. Early identification remains challenging, as current diagnostic approaches [...] Read more.
Background/Objectives: Precocious puberty (PP), defined as the onset of secondary sexual characteristics before 8 years in girls and 9 years in boys, is associated with psychosocial distress, compromised adult height, and long-term metabolic risk. Early identification remains challenging, as current diagnostic approaches are largely reactive and rely on invasive or resource-intensive testing. This narrative review examines how artificial intelligence (AI) can support earlier risk prediction and detection of PP through integration of clinical, hormonal, imaging, lifestyle, and environmental data. Methods: A narrative literature review was conducted using PubMed, Scopus, Embase, Web of Science, and Google Scholar to identify relevant studies published between 2005 and 2025. Eligible studies included original research and high-quality reviews that examined AI-based approaches, such as machine learning and deep learning, in pediatric endocrinology, particularly for the prediction or diagnosis of central or peripheral precocious puberty. Studies incorporating clinical, hormonal, radiological, lifestyle, environmental, or multi-omics data relevant to AI modeling were included. Results: AI models, including XGBoost, random forest, convolutional neural networks, and regression-based approaches, have demonstrated potential utility in predicting central precocious puberty using hormonal, imaging, and growth data. Reported applications include automated bone age assessment, lifestyle and dietary risk stratification, and exploratory use of wearable-derived behavioral data. However, progress is limited by small pediatric datasets, population bias, limited interpretability, and unresolved ethical challenges related to privacy, consent, and equity. Conclusions: Artificial intelligence represents a promising decision-support approach for earlier, non-invasive, and individualized risk assessment in precocious puberty. Future progress will depend on the integration of longitudinal, multimodal data, the development of ethical models, and interdisciplinary collaboration among pediatric endocrinologists, data scientists, and public health stakeholders. Full article
(This article belongs to the Special Issue Game-Changing Concepts in Reproductive Health)
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