000			01937cam a2200157 4500500
005			20251012013219.0
041			_afre
042			_adc
100	1	0	_aPollet-Villard, Xavier _eauthor
245	0	0	_aArtificial intelligence, decision-support agents, and the automation of assisted reproductive technology laboratories: Towards an in vitro fertilization operating system
260			_c2025.
500			_a29
520			_aDecision-support tools based on artificial intelligence (AI) algorithms are gradually being developed in all areas of medicine. The large amount of clinical and laboratory data involved in managing infertility makes reproductive medicine a prime testing ground for AI agents, whose ability to detect linear and non-linear associations through the simultaneous analysis of vast amounts of data exceeds the processing capacity of the human brain. In vitro fertilization remains a largely manual activity. Much like artisans, practitioners are expected to acquire deep practical skills through years of hands-on training and mentoring—whether in handling gametes and embryos or in performing ovarian stimulation, puncture, and embryo transfer. This know-how must be combined with communication skills, empathetic management of doctor–patient relationships, data management and processing, and compliance with standards and regulations—all within a context of a shortage of trained practitioners, rising costs, and declining funding for fertility care. This narrative review describes the use cases of AI and clinical decision-support agents in reproductive medicine, as well as their contribution to the automation and development of an “operating system” for medically assisted reproduction.
786	0		_nMédecine de la Reproduction | 27 | 2 | 2025-09-11 | p. 162-177 | 2650-8427
856	4	1	_uhttps://shs.cairn.info/journal-medecine-de-la-reproduction-2025-2-page-162?lang=en&redirect-ssocas=7080
999			_c1528761 _d1528761