Study of comparative performance of general-purpose LLM-based systems in predicting IVF outcomes

In this exploratory proof-of-concept setting, general-purpose AI systems showed variable and overall suboptimal performance in predicting IVF outcomes from standardized clinical vignettes. Although certain models demonstrated relative strengths in specific tasks, none reached the reliability, consistency, or interpretability required for safe clinical implementation. These findings indicate that, in their current form, such models should not be used as clinical decision-support tools for IVF decision-making and that their use should remain restricted to carefully controlled research settings until they have been prospectively validated in multicenter cohorts and systematically compared with rigorously developed, task-specific prediction models. This study provides comparative insight into how these AI systems behave in IVF-related prediction tasks and underscores the need for cautious interpretation of AI-generated outputs.