What Are the Applications of AI in Genetic Testing?

Author: Rasit Dinc

Introduction

Artificial intelligence (AI) is rapidly transforming various fields of medicine, and genetics is no exception. The ability of AI to analyze vast and complex datasets is providing unprecedented opportunities to unlock the secrets of the human genome and revolutionize the diagnosis, treatment, and prevention of genetic diseases. This article explores the key applications of AI in genetic testing, highlighting how these technologies are enhancing our understanding of genetic variation and paving the way for a new era of personalized medicine.

Enhancing Variant Interpretation

One of the most significant contributions of AI in genetic testing is its ability to enhance the interpretation of genetic variants. The human genome contains millions of genetic variants, and determining which of these are benign and which are pathogenic is a major challenge. AI-powered tools, particularly those based on machine learning (ML) and deep learning (DL), can analyze large-scale genomic and clinical data to predict the functional impact of genetic variants and prioritize them for further investigation [1]. These tools can learn to recognize the subtle patterns that distinguish pathogenic variants from benign ones, enabling more accurate and efficient variant classification. For instance, a type of ML algorithm called support vector machines is used in the CADD (Combined Annotation Dependent Depletion) method to score the deleteriousness of single nucleotide variants and insertion/deletions in the human genome [3].

Accelerating a Diagnosis

AI is also playing a crucial role in accelerating the diagnostic process for rare genetic diseases. The diagnostic odyssey for patients with rare diseases can be long and arduous, often involving numerous tests and specialist consultations. AI-based platforms can analyze a patient's clinical features and genomic data to identify potential candidate genes and variants, significantly shortening the time to diagnosis. For example, AI algorithms can analyze medical images, such as brain MRIs, to identify subtle structural abnormalities that may be indicative of a specific genetic syndrome. By integrating clinical and genomic data, AI can help clinicians to connect the dots and arrive at a diagnosis more quickly and accurately [2].

Personalizing Medicine

The integration of AI and genomics is a cornerstone of personalized medicine. By analyzing a patient's unique genetic makeup, AI can help to predict their risk of developing certain diseases, their likely response to different drugs, and their optimal course of treatment. In oncology, for example, AI algorithms can analyze the genomic profile of a tumor to identify the most effective targeted therapies, leading to more precise and effective cancer treatments. This approach is not limited to cancer; it is also being used to personalize treatments for a wide range of other conditions, from cardiovascular disease to psychiatric disorders.

The Future of AI in Genetic Testing

The applications of AI in genetic testing are still in their early stages, but the potential is immense. In the future, we can expect to see AI-powered tools that can predict the risk of complex, multifactorial diseases, such as diabetes and heart disease, based on a combination of genetic and environmental factors. AI will also be instrumental in the development of new gene-editing technologies, such as CRISPR-Cas9, by helping to identify the most effective and safest ways to correct disease-causing mutations [1]. However, there are also challenges to overcome. The lack of diversity in genomic datasets is a major concern, as it can lead to biased AI algorithms that are less accurate for underrepresented populations. It is therefore crucial to ensure that genomic data is collected from diverse populations to ensure that the benefits of AI in genetics are accessible to all [2, 3].

Conclusion

AI is poised to revolutionize the field of genetic testing, offering powerful new tools to interpret genomic data, accelerate diagnosis, and personalize medicine. While there are still challenges to address, the continued development of AI technologies, coupled with a commitment to ethical and equitable practices, will undoubtedly lead to a future where genetic information is used to improve the health and well-being of people everywhere.

References

[1] Vilhekar, R. S., & Rawekar, A. (2024). Artificial Intelligence in Genetics. Cureus, 16(1), e52035. https://doi.org/10.7759/cureus.52035

[2] American Society of Human Genetics. (2025). Artificial Intelligence and Genetic Testing. https://www.ashg.org/wp-content/uploads/2025/04/Discover-Genetics-AI-Fact-Sheet.pdf

[3] Duong, D., & Solomon, B. D. (2025). Artificial intelligence in clinical genetics. European Journal of Human Genetics, 33(3), 281–288. https://doi.org/10.1038/s41431-024-01782-w