Transformative impact of artificial intelligence (AI) on laboratories, particularly in the realm of genetic research has enchanced the efficiency of labs by automating the interpretation and reporting of genetic data. This process is traditionally reliant on manual, time-consuming tasks performed by Cytogenetic Technologists. AI is particularly beneficial in cytogenetic research, where it accelerates chromosome banding analysis—a critical method for identifying genetic abnormalities in areas like prenatal, postnatal, and cancer research. Old methods are still being used such as microscopes to confirm if the chromosome abnormality is in fact real.
AI advancements in genetic analysis technologies such as next-generation sequencing (NGS), which includes exome sequencing (ES) and whole genome sequencing (WGS). These methods are becoming more accessible and cost-effective, although they still face challenges such as the interpretation of vast data volumes and the potential to miss genetic variants not covered by the tests. While older than NGS, CMA continues to evolve, improving in aspects like sample efficiency and resolution of copy number analysis, thus facilitating quicker and more conclusive results. This supports labs in boosting their throughput and efficiency, making the technology both a robust and a contemporary choice for genetic testing.
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