Like many other medical specialties, clinical genetics is on the brink of transformation with the integration of artificial intelligence (AI). As precision medicine continues to evolve, the field faces a growing need to manage and interpret vast volumes of complex genomic data. Traditional approaches, though foundational, are increasingly insufficient in keeping pace with this rapidly expanding, data-intensive environment. In response, AI—particularly machine learning and deep learning—is emerging as a powerful and transformative tool.

In the paper “Artificial Intelligence in Clinical Genetics: A Review” (full PDF attached below), the authors examine how AI is reshaping the way we diagnose, interpret, and manage genetic conditions. From variant classification to the integration of genomic and clinical data for risk prediction, AI is streamlining clinical workflows, enhancing diagnostic accuracy, and enabling more personalized approaches to patient care. The review also highlights the growing role of natural language processing in extracting insights from unstructured medical records, alongside advanced neural networks capable of detecting subtle, clinically relevant patterns in DNA sequences.

While the opportunities are significant, the paper also emphasizes important challenges - such as bias in training data, lack of model interpretability, ethical considerations, and regulatory hurdles - that must be addressed to ensure the responsible and effective implementation of AI in clinical genetics.