What Are the Applications of AI in Genetic Testing?

Author: Rasit Dinc

Artificial intelligence (AI) is rapidly transforming the landscape of medicine and biomedical research. Among the various medical fields, clinical genetics is particularly poised to be significantly impacted by AI. The growing power and accessibility of AI tools are set to revolutionize how we diagnose, manage, and treat genetic disorders. This article explores the current and emerging applications of AI in genetic testing, delving into its role in clinical diagnostics, management and therapeutics, and clinical support.

AI in Clinical Diagnostics

One of the most significant applications of AI in genetic testing lies in enhancing clinical diagnostics. The diagnostic process in genetics often involves a complex interplay of clinical observations and molecular findings. AI, particularly machine learning (ML) and deep learning (DL), offers powerful tools to analyze vast and complex datasets, thereby improving diagnostic accuracy and efficiency.

Computer vision, powered by DL algorithms, has made remarkable strides in medical imaging. For instance, AI models can analyze medical images, such as retinal scans, to detect signs of genetic disorders that might be missed by the human eye [1]. A notable example is the use of facial analysis technology to identify potential genetic syndromes based on facial phenotypes. Tools like Face2Gene leverage DL to provide differential diagnoses, proving especially valuable in regions with limited access to clinical genetics expertise [2].

Furthermore, AI is instrumental in the analysis of genomic data from next-generation sequencing (NGS) technologies. AI-powered algorithms can sift through enormous volumes of genetic data to identify and classify genetic variants with greater speed and accuracy than manual methods. This automation of variant analysis significantly reduces the time required for diagnosis, from weeks to mere minutes, accelerating the pace of genetic testing and enabling more timely clinical interventions [3].

AI in Management and Therapeutics

Beyond diagnostics, AI is also playing a crucial role in the management and treatment of genetic conditions. By integrating various data types, including genomic, clinical, and imaging data, AI can help predict disease progression and treatment response. This enables clinicians to develop more personalized and effective treatment plans for patients with genetic disorders.

AI is also accelerating the discovery of new therapeutics. For example, AI models can be used to identify new drug targets and design novel therapies for genetic diseases. The ability of AI to analyze complex biological data and identify patterns that are not apparent to humans is a key advantage in this area. By uncovering the molecular mechanisms underlying genetic disorders, AI can help researchers develop targeted therapies that address the root cause of the disease.

AI in Clinical Support

AI-powered tools are also providing valuable clinical support to healthcare professionals in the field of genetics. Large language models (LLMs) and other generative AI technologies are being used to extract and synthesize information from clinical notes, research articles, and other text-based sources. This can help clinicians stay abreast of the latest research and make more informed decisions.

Moreover, AI-driven chatbots and virtual assistants are being developed to provide information and support to patients and their families. These tools can answer questions about genetic conditions, explain complex genetic concepts in simple terms, and provide guidance on managing their health. By empowering patients with information, AI can help them become more active participants in their own care.

The Future of AI in Genetic Testing

The integration of AI into genetic testing is still in its early stages, but its potential is undeniable. In the short term, we can expect to see wider adoption of AI-powered tools for variant interpretation and clinical decision support. In the medium term, AI is likely to become an indispensable tool for drug discovery and development for genetic disorders. In the long term, AI may even enable us to predict and prevent genetic diseases before they manifest.

However, the increasing reliance on AI in genetic testing also raises important ethical and regulatory questions. Issues such as data privacy, algorithmic bias, and the role of human oversight need to be carefully addressed to ensure that AI is used responsibly and equitably. As we move forward, it will be crucial for all stakeholders, including clinicians, researchers, patients, and policymakers, to work together to navigate the challenges and opportunities presented by AI in genetic testing.

References

[1] 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

[2] Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., et al. (2024). Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open, 7(7), e2440969. https://doi.org/10.1001/jamanetworkopen.2024.40969

[3] 3billion. (2025, March 6). AI in Genetic Testing: How 3billion is Revolutionizing Rare Disease Diagnosis. 3billion. https://3billion.io/blog/ai-in-genetic-testing-how-3billion-is-revolutionizing-rare-disease-diagnosis