Real-world use of an artificial intelligence–powered clinical decision support tool for ovarian stimulation

• Published in: F&S Reports (Online) Vol. 6; no. 2; pp. 140 - 146
• Main Authors: Bixby, Cameron J., Miller, Bradley
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2025; Elsevier
• Subjects: Artificial intelligence; machine learning; Obstetrics and Gynecology; Original; ovarian stimulation; ART; machine learning; Artificial intelligence; ovarian stimulation
• ISSN: 2666-3341; 2666-3341
• DOI: 10.1016/j.xfre.2025.01.015

To understand how treatment decisions and patient outcomes change with physician utilization of artificial intelligence (AI) to help determine follicle-stimulating hormone (FSH) starting dose and trigger injection timing during ovarian stimulation. Retrospective cohort study with historical controls. Patients undergoing ovarian stimulation by multiple physicians at one in vitro fertilization clinic in the United States. A total of 292 patients were treated between December 2022 and December 2023 with adjunctive clinician use of AI to help select starting dose of FSH and timing of the trigger injection. These were matched to 292 historical control patients treated between May 2019 and May 2022 by the same physicians without AI. The primary endpoints were the starting FSH dose, total FSH dose, and number of metaphase II (MII) oocytes retrieved at the end of stimulation. The use of AI did not introduce any adverse events. After patient matching, there were no statistically significant differences in age, body mass index, antimüllerian hormone, or antral follicle count between the treatment and control groups. Comparing the treatment arm with the control arm, the average number of MII oocytes was 11.17 vs. 11.25, the average starting FSH dose was 397.09 IU vs. 443.84 IU, and the average total FSH dose was 4,181.77 IU vs. 4,654.71 IU. Physician use of AI helped significantly reduce the starting and total FSH doses prescribed to patients without adversely affecting MII outcomes, indicating the potential use of AI in lowering in vitro fertilization costs to patients.