Genomics are the foundations of precision medicine. The completion of the Human Genome Project in 2003 marked the beginning of a new era in healthcare, revealing how genetic variations influence disease diagnose, drug metabolism, and treatment response.

The impact of genomics on clinics is profound. Pharmacogenomic testing can guide dosing decisions for critical medications, preventing adverse reactions and treatment failures (3, 4). In oncology, tumor genomic profiling has revolutionized cancer care, enabling targeted therapies that specifically attack cancer cells based on their genetics (5). Hereditary cancer syndromes, and other genetic diseases can now be diagnosed by genetic techniques, allowing for early intervention and family screening (6). In neurology, researchers developed technique to detect biomarker of demyelinating disease (7). And for an example in infectious disease, researchers developed VirCapSeq-VERT, a positive selection system based on genetics for detecting RNA and DNA viruses (8).

However, genomics also showed the limitations of genetic determinism. The complex interactions between genes, environment, and human body means that genetic information alone cannot fully predict health conditions or optimal treatments. This realization has driven the start toward multi-modal approaches that consider genomics as the cornerstone while recognizing the need for other layers of biological and clinical information.

While genomics is foundation for precision medicine, we believe that genes alone can tell only part of the story. The human body is an intricate system where genetic intersects with environmental factors, lifestyle choices, and organs. Multi-modal AI can be one of our most powerful techniques to explore this complexity.

Unlike traditional AI systems that focus on single data types, multi-modal AI can simultaneously process and integrate genomics sequences, medical imaging, electronic health records, and even social variants of health (see Figure 1). To be more specific, multi-modal AI models incorporate different types of data source and transform them into embedding by joining independent embedding of data sources (9). More recent models using data fusion layer to fuse multiple embedding into one embedding for downstream tasks (10), such as predicting diagnostics. This approach mirrors how physicians naturally think about patients—not as isolated genetic data, but as complex individuals whose health comes from multiple interacting factors.

Consider cancer treatment, where this convergence is already yielding remarkable results. Multi-modal AI systems can analyze tumor genomics alongside histopathological images, treatment history, and biomarkers to predict which patients will respond to specific immunotherapies (11, 12). Companies such as Tempus and Foundation Medicine are pioneering platforms that combine genomic profiling with clinical data analysis, enabling oncologists to make treatment decisions based on comprehensive molecular information. In cardiology, researchers have developed models that integrate genetics with risk variants of patients (13, 14). In neurology, researchers developed a multi-modal screening system for elderly neurological diseases (15).

Multi-modal approaches are particularly powerful for addressing health disparities. By incorporating social determinants of health alongside genomic data, these systems can better account for the environmental and socioeconomic factors that significantly influence health outcomes in different populations (16, 17). This is crucial for ensuring that precision medicine benefits all patients, not just those in affluent healthcare systems.