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Evaluation of Satisfaction With a Secure, Connected Mobile App for Women in Assisted Reproductive Technology Programs: Prospective Observational Study

In 2018, the European Society of Human Reproduction and Embryology’s 22nd report on medically assisted reproduction (MAR) in Europe highlighted a continuous increase in the number of treatment cycles and the broad range of techniques used [1]. Every day, around the world, thousands of women undergo hormone assays and ultrasound scans of the pelvis as part of their MAR program. These burdensome, complex procedures can generate stress and anxiety for the women and their partners [2-4].

Pauline Plouvier, Romaric Marcilly, Geoffroy Robin, Chaymae Benamar, Camille Robin, Virginie Simon, Anne Sophie Piau, Isabelle Cambay, Jessica Schiro, Christine Decanter

JMIR Hum Factors 2025;12:e63570

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Net Reproduction Number as a Real-Time Metric of Population Reproducibility

Therefore, the net reproduction rate (Rt), the number of daughters a woman of childbearing age would produce under prevailing fertility and mortality conditions, is better. Like other real-time epidemiological metrics (eg, the effective reproduction number in infectious disease modeling) [5], the Rt can be calculated and updated regularly with new population data; it can provide timely insights into population sustainability.

Chiara Achangwa, Changhee Han, Jun-Sik Lim, Seonghui Cho, Sangbum Choi, Sukhyun Ryu

JMIR Public Health Surveill 2025;11:e63603

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Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database

reproduction

Mackenzie Lemieux, Cyrus Zhou, Caroline Cary, Jeannie Kelly

JMIR Infodemiology 2024;4:e64577

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Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review

The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction Reference 46: Follitropin alpha for assisted reproduction: an analysis based on a non-interventional longitudinal follicular growth tracking during IVF cycle using 3D transvaginal ultrasound in assisted reproduction Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction Reference 87: Artificial intelligence and machine learning for human reproduction and embryology presentedreproduction

Rawan AlSaad, Alaa Abd-alrazaq, Fadi Choucair, Arfan Ahmed, Sarah Aziz, Javaid Sheikh

J Med Internet Res 2024;26:e53396

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