AI and ML are increasingly transforming the practise of medicine across multiple disciplines. Significant inroads have also been made in disciplines where identification and classification of patterns are central to the practise of dermatology7, radiology8 and pathology.
Fremont, CA: Infertility rates in the United States vary from 6 to 18%; however, couples undergo successful infertility care at rates of less than 1%.1,2 The main obstacles to the wider use of Assisted Reproductive Technology (ART) are lack of access, high costs, difficulty of treatment, and low success rates. 3.4 despite decreasing fertility rates in Western countries5, relatively few resources have been allocated to reproductive science. For example, NIH funds allocated to contraception outpaces infertility research 4 to 5-fold. In reality, funding for reproductive research contributes to several other diseases with lower prevalence. The field is primed for innovation.
Machine Learning (ML) are systems that can learn from large quantities of clinical, demographic, anatomy, laboratory and imaging data to make correlations and suggestions that humans cannot easily detect. 6 In essence, ML is a collection of algorithms that learn how to learn by looking at patterns and correlations in large datasets. Learning can be driven or autonomous, but it generates outputs that can be used to direct decisions—including decisions on health care.
AI and ML are increasingly transforming the practise of medicine across multiple disciplines. Significant inroads have also been made in disciplines where identification and classification of patterns are central to the practise of dermatology7, radiology8 and pathology. 9 As a field, reproductive science has been slow to explore opportunities in AI for reasons that are not at all obvious. AI has a remarkable ability to address barriers to cost, access and low success rates.
Consider ART's extremely manual and labour-intensive processes as they are today. Success rates depend on a variety of variables. Such variables are patient-specific and uncontrollable, but several others are embedded in the mechanism, including semen, oocyte, and embryo selection for implant fertilisation. Lack of automation results in high inter-user variability. Indeed, talented embryologists can be very effective after years of training and practise; however, the learning curve and variability across providers are rate-limiting. These variables are also a source of substantial functional costs. Automation and streamlining of the whole process can minimise operating costs for fertility practises and improve access and reduce costs for patients. Innovation does not decrease revenue for clinicians—quite the opposite—increased access, more effective processing, and improved results are projected to increase patient volume and revenue while decreasing manual workload.
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