Generative AI and Language Models in Human Genetics and Health: From Variant Interpretation to Clinical Decision...

Generative artificial intelligence (AI) is transforming biological and medical research and data analysis. Beyond analyzing existing information, these models can learn complex patterns and generate new data such as realistic protein sequences, genetic variants, or clinical notes. In molecular biology, language-like sequence models can read and generate DNA, RNA, and amino acid sequences to predict genetic variant effects, design new proteins, and explore molecular functions. In medicine, large language models (LLMs) trained on biomedical literature and electronic health records (EHRs) can summarize clinical findings, identify patterns, and provide decision support for clinicians and healthcare providers. Additionally, synthetic data generation can help protect patient privacy and augment existing disease datasets. While these advances make tasks that were previously impractical possible at scale, they also carry major risks, including producing convincing but incorrect results, reflecting hidden biases in the training data, and underperforming when real-world conditions change.