Real-world implementation of AI-assisted starting gonadotropin dosing in routine ovarian stimulation: an exposure-defined, propensity score-matched observational cohort study

Introduction Starting gonadotropin dose selection in controlled ovarian stimulation is conventionally guided by clinician experience. This pilot, single-centre, exposure-defined, matched observational cohort study assessed the feasibility of deploying an artificial intelligence-assisted gonadotropin dosing tool (POvaStim) in routine practice, comparing clinicians using the tool versus conventional experience-based judgement. Because AI use was completely confounded with clinician group and clinician experience was not prospectively quantified, the independent contribution of the decision-support tool cannot be isolated. Methods Consecutive women undergoing IVF/ICSI with a GnRH-antagonist protocol were treated between August 3 and December 31, 2022; only the starting dose was AI-assisted. One-to-one propensity score matching on body weight, ovarian reserve score and gonadotropin preparation yielded 119 matched pairs. Primary outcomes were cumulative clinical pregnancy and live birth per oocyte retrieval (data-lock 31 December 2024). Results Cumulative clinical pregnancy occurred in 67 of 119 (56.3%) in the AI-assisted arm versus 76 of 119 (63.9%) in the control arm (risk difference −7.6%, 95% CI −20.0 to +4.8; P = 0.234), and cumulative live birth in 59 of 119 (49.6%) versus 60 of 119 (50.4%) (risk difference −0.8%, 95% CI −13.5 to +11.9; P = 0.897), counting the 16 women without an ascertainable outcome by the data-lock date as non-events. A complete-case analysis excluding these 16 women gave materially unchanged results. These wide confidence intervals do not exclude clinically important differences; the study was not an equivalence or non-inferiority trial. Starting-dose selection was substantially less variable in the AI-assisted arm (interquartile range 43% narrower; variance 2.43-fold lower, 95% CI 1.69–3.50). Gonadotropin exposure was nonetheless higher in the AI-assisted arm (+27.4 IU/day). POvaStim recommended similar doses in both arms: the AI-assisted dose was rounded upward from it in 94.1% of cycles, while control-arm clinicians, unaware of the recommendation, more often prescribed below it. Elective freeze-all, chiefly for hyperstimulation prevention, was more frequent in the AI-assisted arm (46.2% versus 34.5%; risk difference +11.8%; P = 0.064), although none developed severe hyperstimulation requiring admission; the study was not powered for safety endpoints. Discussion AI-assisted starting-dose selection was feasible, with less variable dosing, but given the confound between AI assistance and clinician experience, these differences cannot be attributed to the tool itself.

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Journal
Frontiers in Endocrinology
Published
2026-11-05
DOI
https://doi.org/10.3389/fendo.2026.1944764
Primary Topic
Reproductive Health and Technologies
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article
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article

Real-world implementation of AI-assisted starting gonadotropin dosing in routine ovarian stimulation: an exposure-defined, propensity score-matched observational cohort study

Hongbin Chi, Guoshuang Feng, Rui Yang, Jie Qiao et al.
Frontiers in Endocrinology
Reproductive Health and Technologies
article

Real-world implementation of AI-assisted starting gonadotropin dosing in routine ovarian stimulation: an exposure-defined, propensity score-matched observational cohort study

Hongbin Chi, Guoshuang Feng, Rui Yang, Jie Qiao, Congcong Ma, Huiyu Xu, Yaodong Zhang, Shuo Yang, Lina Wang, Rong Li
article en

Abstract

Introduction Starting gonadotropin dose selection in controlled ovarian stimulation is conventionally guided by clinician experience. This pilot, single-centre, exposure-defined, matched observational cohort study assessed the feasibility of deploying an artificial intelligence-assisted gonadotropin dosing tool (POvaStim) in routine practice, comparing clinicians using the tool versus conventional experience-based judgement. Because AI use was completely confounded with clinician group and clinician experience was not prospectively quantified, the independent contribution of the decision-support tool cannot be isolated. Methods Consecutive women undergoing IVF/ICSI with a GnRH-antagonist protocol were treated between August 3 and December 31, 2022; only the starting dose was AI-assisted. One-to-one propensity score matching on body weight, ovarian reserve score and gonadotropin preparation yielded 119 matched pairs. Primary outcomes were cumulative clinical pregnancy and live birth per oocyte retrieval (data-lock 31 December 2024). Results Cumulative clinical pregnancy occurred in 67 of 119 (56.3%) in the AI-assisted arm versus 76 of 119 (63.9%) in the control arm (risk difference −7.6%, 95% CI −20.0 to +4.8; P = 0.234), and cumulative live birth in 59 of 119 (49.6%) versus 60 of 119 (50.4%) (risk difference −0.8%, 95% CI −13.5 to +11.9; P = 0.897), counting the 16 women without an ascertainable outcome by the data-lock date as non-events. A complete-case analysis excluding these 16 women gave materially unchanged results. These wide confidence intervals do not exclude clinically important differences; the study was not an equivalence or non-inferiority trial. Starting-dose selection was substantially less variable in the AI-assisted arm (interquartile range 43% narrower; variance 2.43-fold lower, 95% CI 1.69–3.50). Gonadotropin exposure was nonetheless higher in the AI-assisted arm (+27.4 IU/day). POvaStim recommended similar doses in both arms: the AI-assisted dose was rounded upward from it in 94.1% of cycles, while control-arm clinicians, unaware of the recommendation, more often prescribed below it. Elective freeze-all, chiefly for hyperstimulation prevention, was more frequent in the AI-assisted arm (46.2% versus 34.5%; risk difference +11.8%; P = 0.064), although none developed severe hyperstimulation requiring admission; the study was not powered for safety endpoints. Discussion AI-assisted starting-dose selection was feasible, with less variable dosing, but given the confound between AI assistance and clinician experience, these differences cannot be attributed to the tool itself.

Frontiers in EndocrinologyVol. 17
Capital Medical University (CN), National Clinical Research (US), Peking University Third Hospital (CN), Beijing Children’s Hospital (CN)
Openalex Percentile: Top 14%
Reproductive Health and Technologies
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