AI-driven support to improve patient engagement during IVF
Official title: Assessing the Impact of an Artificial Intelligence-Machine Learning Model on Patient Engagement in Medically Assisted Reproduction
Run by Instituto Valenciano de Infertilidade de Lisboa · NCT07087171 · Data updated 2026-11-12
This project will test whether an AI tool that provides personalized live-birth probabilities and tailored messages helps people aged 18–45 undergoing IVF/ICSI or donor cycles stay engaged with treatment and follow clinic recommendations.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 45 Years |
| Sex | All |
| Enrollment | 774 (estimated) |
| Sponsor | Instituto Valenciano de Infertilidade de Lisboa Research network |
| Locations | 1 site (Lisbon) |
| Status | Recruiting |
| Started | June 11, 2025 |
| Main results expected | June 2026 |
| Study ends | August 2026 |
| Registry record updated | February 23, 2026 |
| Trial ID | NCT07087171 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are infertile individuals aged 18–45 planning IVF/ICSI, intrauterine insemination, or oocyte donation who are willing to receive counseling about their personalized live-birth probability.
✕Probably not a fit
Patients older than 45, those not eligible for IVF/ICSI, menopausal or peri‑menopausal individuals, those undergoing fertility preservation, people who decline counseling about live‑birth probabilities, and same‑sex couples receiving partner oocytes are unlikely to benefit from this intervention.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study at IVI‑RMA Lisboa will deliver AI/ML reports that provide individualized probabilities of live birth and tailored communication to patients undergoing medically assisted reproduction. Researchers will track engagement metrics such as treatment initiation, appointment attendance, medication adherence, dropout rate, conversion rate, and patient-reported stress and satisfaction. Eligible participants are infertile patients aged 18–45 who plan IVF/ICSI, intrauterine insemination, or oocyte donation and are willing to receive counseling about their personalized prognosis. The study will compare engagement outcomes before and after introduction of the AI reports, using historical or concurrent controls as available.
Why it matters
Potential benefit: If successful, this approach could give patients clearer personalized chances of success plus tailored reminders and education that help more people start and complete treatment, reduce dropouts, and lower stress.
How similar studies have gone: Previous AI models have improved prediction of IVF outcomes and aided counseling in some reports, but applying AI specifically to increase patient engagement and conversion rates is relatively novel with limited published evidence.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Infertile patients aged 18-45 years * Patients willing to undergo Medically Assisted Reproduction (heterosexual couples, same-sex female couples and single females undergoing artificial insemination, IVF/ICSI or oocyte donation treatments) Exclusion Criteria: * Age \>45 years * Patients who are not candidates for IVF/ICSI * Patients who are menopausal or peri-menopausal * Patients undergoing Fertility Preservation * Same-sex couples who will undergo reception of oocytes from partner. * Patients who decline to be counselled about their probability of having a live birth from IVF/ICSI treatment
Where it's running
-
IVI-RMA Lisboa
Lisbon, Portugal Recruiting
Study contacts
- Study coordinator: Ana R Neves, MD, PhD
- Email: ana.neves@ivirma.com
- Phone: +351 800 180 614
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.