This commentary examines the growing use of AI tools focused on embryo selection in IVF, while arguing that treatment discontinuation — patients stopping IVF before achieving pregnancy — remains largely unmeasured and unaddressed.
The paper contends that optimizing the embryo laboratory with AI represents an incomplete approach to IVF success, as treatment dropout is a major barrier to cumulative live birth rates but lacks standardized measurement or AI-driven solutions.
This is a commentary without primary data; no empirical sample, comparator group, or outcome statistics are reported.
Clinicians and IVF programs should track treatment discontinuation rates alongside embryology metrics. Relying solely on AI embryo tools risks missing a key driver of poor cumulative outcomes: patients who stop treatment before success.
