AI has the potential to revolutionize the field of genomic diagnostics. Genetic diagnostics could benefit from the use of AI in healthcare.
FREMONT, CA: Despite a few setbacks, AI in healthcare has mostly benefited both the sector and technology. AI's unmatched capacity to sift through massive amounts of data and provide clarity for decision-making via its pattern and anomaly recognition capabilities makes it suitable for a field as diverse and challenging as healthcare.
The application of AI to diagnose diseases caused by genetic abnormalities is still in its infancy. Numerous corporations and healthcare organizations have created AI applications to diagnose genetic illnesses. Generally, each of these applications has the same objective—to decrease the time required to conduct genome-based diagnostics on patients while simultaneously eliminating human error. Understanding these applications enables the healthcare sector to forecast how AI will evolve in this subject in the future.
Machine learning (ML) is a compelling method for detecting anomalies in an individual's genetic structure. As a result, many applications for gene-based diagnostics incorporate an AI component to ensure the most accurate findings are obtained. For example, healthcare professionals and researchers at the Rady Children's Institute for Genomic Medicine (RCIGM) have created a ML-based process that incorporates Natural Language Processing (NLP) to rapidly and accurately diagnose genetic illnesses. This procedure monitors infants' genomic sequencing to determine whether they have any unusual genetic abnormalities.
The process's critical specialty is rapid genomic sequencing, with the institution setting a Guinness World Record in February 2018 for completing high-speed genomic diagnostics on a test subject in an average of 19 hours utilizing the augmented intelligence approach. The accomplishment was proclaimed at a press conference in San Francisco and another in Orlando, drawing large crowds. The rapid Whole Genome Sequencing (rWGS) procedure is optimized to reduce the time required to detect genetic abnormalities in test subjects' blood samples.
Despite the increased diagnostic speed, the accuracy of a genomic diagnosis is maintained, and the method routinely achieves up to 99 percent precision in the genome sequencing, phenotyping, and interpretation of genetic data in test subjects' blood samples. Such extraordinarily high diagnostic speed and accuracy levels are critical for the future of AI in healthcare and for providing timely, precise medical care in high-pressure intensive care unit operations. Genetic disorders are among the top causes of infant death worldwide. As indicated previously, delays in diagnosing such conditions can result in many preventable fatalities among youngsters. The procedures of genomic data interpretation and gene sequencing can be accelerated using rWGS and other related tools and machine learning-based technologies. Additionally, as AI and other technologies become more available to the general public, gene-based diagnostics utilizing such systems can become more economical.