Every year, numerous children are born with severe genetic disorders, with a considerable number remaining undiagnosed due to the complexity of identifying causative genetic variants. The emergence of artificial intelligence (AI) in genetic diagnosis offers a promising avenue for addressing these challenges, pushing the boundaries of precision medicine.
Genetic disorders, often resulting from single-gene mutations, present a significant diagnostic challenge. Despite modern bioinformatics tools, the process of pinpointing the exact variant responsible for a disease remains a complex task, demanding both time and extensive knowledge. Despite the advent of sophisticated bioinformatics tools, the diagnostic rate for genetic disorders hovers between 30 and 40%. This complexity underscores the need for more efficient diagnostic methodologies.
AI systems, such as AI-MARRVEL (AIM), have been developed to enhance the diagnostic process for Mendelian disorders. These systems leverage patient clinical features and sequencing profiles, offering a sophisticated approach to variant prioritisation. AIM, in particular, is trained on a more than 3.5 million variant data points and curated by certified experts, embodying a significant leap forward in genetic diagnostics.
While AI systems like AIM mark a significant advancement, they are not without limitations. The inability to analyse certain types of genetic variations and a focus on coding variants restrict their scope. However, these challenges present opportunities for further innovation and integration of more comprehensive analytical tools.
AI in genetic diagnosis is not just a theoretical concept but a practical tool with a web interface for real-world application. It has the potential to truly enhance the periodic reanalysis of undiagnosed cases, making it a feasible and cost-effective option for many. As AI continues to evolve, its integration into clinical practice promises to enhance patient outcomes and set a new norm in genetic medicine.
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