This podcast discusses the research publication by Yao et al, Journal of Clinical Medicine 2024, titled "Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience".

Briefly, Univfy and their collaborators examined the impact of patient-centric artificial intelligence and machine learning (AI/ML) tools on the utilization of In Vitro Fertilization (IVF) services.

Check out:

• Dr. Mylene Yao's related LinkedIn post: https://bit.ly/4jkll2N
• The actual research article: https://bit.ly/3Z2c6gl

Created with Google Notebook LM. Content reviewed and approved by Univfy.

This podcast discusses the research performed by Univfy and their collaborators to examine the impact of patient-centric artificial intelligence and machine learning (AI/ML) tools on the utilization of In Vitro Fertilization (IVF) services. Researchers conducted a retrospective study reviewing over 24,000 new fertility patients across 7 fertility centers at over 30 locations in US and Canada to determine if using an AI-powered prognostic report, specifically the Univfy report, was associated with a higher rate of patients starting IVF treatment. 

The findings suggest that exposure to the Univfy report was indeed linked to an increased likelihood of patients converting to IVF compared to those who did not receive the report. The study also explores factors influencing treatment decisions within the group using the report, noting that older age was a small but independent predictor of IVF initiation after accounting for individual center characteristics.

This podcast summarizes the research findings reported by Yao et al in Nature Communications 2025 (https://www.nature.com/articles/s41467-025-58744-z). 

Check out:

• Dr. Mylene Yao's related LinkedIn post: https://bit.ly/4cLV4ZL
• Univfy press release: https://bit.ly/3RsUHcJ 
• The actual research article: https://go.nature.com/4jGaFfx

Created with Google Notebook LM. Content reviewed and approved by Univfy.

Context: The researchers highlight the significant public health issue of infertility and the existing barriers to affordable and accessible in vitro fertilization (IVF) treatment. 

This research compares machine learning center-specific (MLCS) models developed by Univfy with the US national registry-based model (SART, Society for Reproductive Technology) for predicting live birth outcomes in IVF. The research, published in Nature Communications 2025 , indicates that MLCS models are superior in accuracy and clinical utility, particularly in minimizing false predictions and providing validated and personalized probability of having a live birth from IVF. Over 25% of patients would receive an underestimation of IVF live birth prognostics from the SART model compared to the Univfy models and the prognosis given by the Univfy models are more appropriate in those cases. 

The authors also explain that the improved predictive power of MLCS models has significant implications for patient-centric care, patient counseling, enabling value-based IVF pricing, increasing utilization rates. Collectively, these benefits are also expected to sustain population growth.