Clinical Decision Support Models to Optimize Controlled Ovarian Hyperstimulation: A Scoping Review

Controlled ovarian hyperstimulation (COH) is a precision medicine challenge, with substantial inter-individual variability in ovarian response affecting treatment efficacy and safety during in vitro fertilization (IVF). Clinical decision support (CDS) tools incorporating statistical, machine learning, and artificial intelligence approaches have been developed to individualize gonadotropin dosing and ovulation trigger timing. Therefore, this study aims to identify and evaluate CDS tools informing prescribing decisions during COH. Following PRISMA-ScR guidance, Embase, MEDLINE, PubMed, and Scopus were searched from inception to March 2026. Studies evaluating CDS tools informing COH dosing and/or ovulation trigger timing during IVF were included, stratified by predictive domain and analyzed descriptively. Primary outcomes were oocyte yield and metaphase II (MII) oocyte yield. Secondary outcomes included gonadotropin use and pregnancy-related outcomes. Of 2879 records identified, five studies (n = 16,441) were included. One study reported precision-dosing CDS tools that were associated with increased MII oocyte yield (1.5 oocytes, p < 0.001) and reduced gonadotropin use (195 IU, p < 0.001) compared with clinician-directed dosing. A trigger-timing model improved MII oocyte yield (2.5 oocytes, p < 0.01), whereas combined dosing-and-trigger models demonstrated inconsistent effects. K-nearest neighbor (n = 3) and linear regression (n = 3) approaches predominated. No pregnancy-related outcomes were reported. No clinically evaluated mechanistic pharmacokinetic/pharmacodynamic models were identified. CDS tools show promise for individualizing COH and improving treatment efficiency through precision dosing. However, clinical translation remains limited by a small and heterogeneous evidence base, reliance on surrogate outcomes, and lack of mechanistic model-informed approaches. Prospective studies evaluating clinically meaningful outcomes are required to establish the role of CDS tools in personalized reproductive medicine.