It is an unfortunate truth that in vitro fertilization (IVF) and other forms of assisted reproductive technology (ART) can be cost-prohibitive for many people experiencing infertility. Although IVF has improved over the decades, its success is still not guaranteed—and often, the IVF treatment cost adds insult to injury.
However, one of the most promising advances in ART is the use of artificial intelligence (AI) to increase the chances of a successful pregnancy and reduce the cost of fertility treatment. Experts believe that, when fully realized, AI is likely to change the way patients with infertility are treated, even before IVF begins.
Knowing the Cost of ART Procedures
In 2021, Forbes placed a typical IVF treatment cost at $12,000 to $14,000 but noted that this is just the starting point. Once the patient realizes there are additional costs—such as medications, cryopreservation, embryo storage and genetic testing—a single cycle is more likely to cost between $15,000 and $20,000. Although some clinics have lowered the cost of IVF by cutting their overhead and purchasing wisely, many infertile patients are still unable to afford conceiving a child in this way.
An opinion published in Fertility and Sterility calls ART a "highly manual and labor-intensive process," with success depending on several factors. One of the primary problems leading to low success rates is related to variability in experience and training among embryologists. Clinicians are responsible for the selection of sperm, oocytes and embryos. As the author points out, this variability leads to higher costs for patients, seeing as lower quality embryos often lead to failed attempts.
Using Machine Learning to Reduce IVF Treatment Cost
Instead of a human embryologist choosing embryos based on morphological analysis, what if a machine could do it? This is already happening to some extent, and considerable investments are being made in this promising technology.
Research published in Nature suggests that pregnancy rates would be vastly improved simply with the "ability to select the single best embryo with the highest implantation potential," while acknowledging that maternal and treatment factors must also be assessed.
These needs may be met with further research in AI, according to the journal Reproduction. While AI and machine learning (ML) have shown success analyzing images to determine the quality of sperm and embryos, the large datasets on which AI depends may open the door to better diagnosis and treatment.
Soon, these same datasets will be large enough to yield algorithms that can be applied to clinical decision support systems. Reproductive health specialists will then be able to depend on these systems for evidence-based treatment plans that help reduce unnecessary tests and medications, saving these costs for patients.
The Reproduction research also points out doctors' time can be saved by handing over repetitive tasks to ML. For example, a computer program could choose the best embryos, and the embryologist would only need to confirm the findings. This kind of automation leaves more time for tasks that no machine can handle as well as a human provider, such as compassionately communicating findings with patients.
Looking Ahead for Better Care
Advances in IVF treatment and reductions in IVF treatment costs are still needed. When AI is able to supplement the physician's work by choosing sperm and embryos, physicians will ideally spend less time analyzing morphology and more time with patients. When AI is able to provide algorithm-based treatment plans based on an individual patient's circumstances, the burden of cost will hopefully be reduced. This field is changing rapidly, and these improvements may arrive more quickly than you think.
