AI in the IVF Laboratory: Can It Really Improve Success Rates?

Artificial intelligence is entering a growing number of medical settings, including in vitro fertilization laboratories. In some IVF labs, AI may be used to analyze embryo images, assist with grading, anticipate workload, and support identification or cryostorage management.

These uses raise two separate questions: whether AI can improve laboratory efficiency and whether it has been shown to improve clinical pregnancy or live birth outcomes for patients.

A 2026 committee opinion from the American Society for Reproductive Medicine (ASRM) describes AI in the IVF laboratory as an early-stage field. Current research shows potential, but stronger clinical validation is still needed before widespread adoption.

For laboratory operations, AI may analyze treatment-cycle data and help forecast retrievals or other procedures. Large fertility centers may use this information for staffing and equipment planning, although time and cost savings need to be evaluated by each laboratory.

Embryo grading is traditionally performed by trained embryologists using morphology and developmental information. AI systems can analyze static images or time-lapse videos to assist with grading and ranking, potentially reducing review time and variation among observers.

AI and related automation may also support electronic witnessing, specimen identification, cryostorage management and the reduction of some documentation errors. These quality and workflow functions are not the same as predicting an individual patient's outcome.

Embryo-analysis models are generally trained on large collections of images, videos and clinical outcomes. Researchers may link developmental images with blastocyst formation, ploidy, implantation, clinical pregnancy or other endpoints so an algorithm can identify patterns.

A model trained mainly at one fertility center, in one patient population or under one laboratory protocol may perform differently elsewhere. Image quality, labeling practices, patient mix, culture conditions and clinical procedures can all affect performance.

Strong results in an original dataset therefore do not guarantee the same results across IVF laboratories. Independent validation and testing in real clinical settings remain important.

The clearest potential benefits of AI embryo grading currently involve laboratory efficiency and consistency. Some studies cited by ASRM found substantial reductions in the time required for embryo assessment.

However, grading consistency, predictive accuracy, clinical pregnancy and live birth are different endpoints. A system that ranks images more quickly does not automatically increase a patient's chance of pregnancy or live birth.

Many studies supporting AI-based embryo selection have relied on retrospective datasets. These studies can identify associations, but they cannot by themselves establish that AI improves patient outcomes.

The ASRM opinion discusses a large randomized controlled trial comparing a deep-learning embryo-selection algorithm with standard morphology-based selection. Clinical pregnancy occurred in 46.5% of patients in the AI group and 48.2% in the morphology group.

The difference was not statistically significant, and the trial did not meet its prespecified noninferiority objective. This does not establish that AI has no value; it means the evidence did not demonstrate that this algorithm improved clinical pregnancy outcomes.

Results must also be interpreted in the context of the specific algorithm, patient population, laboratory setting and chosen endpoint. Findings from one product or version cannot be assumed to apply to every AI tool.

AI products can evolve quickly, but the value of a medical technology cannot be determined only from retrospective performance or an accuracy metric. ASRM calls for well-designed prospective studies and randomized controlled trials before widespread clinical adoption.

Research should examine live birth outcomes, safety, laboratory error rates, efficiency, time, cost and performance across different patient groups, rather than focusing only on image classification.

Models also need to demonstrate stable performance across fertility centers, equipment and populations. Improvements in laboratory metrics should not be presented as improvements in patient success rates without supporting evidence.

Some AI systems generate scores or rankings without clearly explaining how the result was reached. This black-box problem raises practical questions about professional review and who makes the final decision when the algorithm and embryologist disagree.

AI development may involve embryo images, patient information and treatment outcomes. Clinics should address how information is stored, whether it is used for model training and what patients are told about those uses.

Regulatory clearance or permission to use a product does not necessarily prove that it improves clinical pregnancy or live birth rates. ASRM also cautions against commercial promotion of clinical benefits that have not been demonstrated.

Patients can ask which part of the laboratory process uses AI, whether it assists an embryologist or directly influences embryo ranking, and whether it analyzes static images or time-lapse data.

Other useful questions include whether the system has been validated in the clinic's own population, whether supporting studies measured grading accuracy, clinical pregnancy or live birth, and how disagreements between the algorithm and human assessment are handled.

Patients may also ask whether the service adds cost, whether their data or embryo images may be used for model training, and whether declining the AI service changes standard treatment.

AI may help IVF laboratories improve efficiency, reduce some subjective variation and provide additional information for embryo assessment. Laboratory efficiency, embryo ranking and eventual live birth, however, are not interchangeable outcomes.

Based on the current ASRM committee opinion, AI adoption in IVF laboratories should proceed cautiously. Current evidence does not support presenting AI as a technology that reliably improves an individual patient's IVF success rate.

This article summarizes publicly available professional information for general educational purposes. It is not medical advice, diagnosis, treatment planning or a guarantee concerning any technology, clinic or individual outcome. Personal fertility and embryo-selection decisions should be discussed with appropriately qualified reproductive medicine professionals.