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Biomedical researchers are using AI to discover vital insights into health and disease. Data and computational scientists are using the complexities of biomedicine to train new AI and machine learning models. Each field is advanced by the other. Ó³»­´«Ã½ is a collaborative ecosystem where this synergy happens.

Analyzing vast datasets

At Ó³»­´«Ã½, researchers produce and share massive amounts of biomedical data, including genetic, cellular, imaging, and electronic health record data. With AI, they are able to analyze and find patterns in the data that are otherwise difficult or impossible to detect.

Finding these patterns allows Ó³»­´«Ã½ researchers to more quickly translate genetic data into the biological causes of disease. AI is also bringing biomedical research into the digital age and making it far more scalable, accelerating the development of better treatments and diagnostics.

Advancing therapeutics research

Most scientific teams across the Ó³»­´«Ã½ either include AI specialists as key members or work closely with one of several AI-focused teams. AI and computational experts routinely collaborate with biologists working in the lab to, for example:

• Pinpoint specific genes, molecules, and cells that are causing diseases such as diabetes, heart disease, cancer, schizophrenia, and bipolar disorder. These could become targets for new treatments.
• Discover new drugs such as antibiotics for antibiotic-resistant infections 
• Predict the structure and function of proteins based on their sequence, which can accelerate drug discovery
• Improve disease diagnosis through better analysis of cellular and clinical images
• Identify which patients will respond best to a treatment 

AI is helping to make lab work more efficient by generating new hypotheses and predicting which experiments are more likely to succeed. This can vastly reduce the time and resources required for new discoveries. 

Building next-generation tools and scientists

Ó³»­´«Ã½ scientists are also building new AI tools and methods, including language models that they make freely available to the wider research community. They are using complex biomedical questions and datasets to innovate new AI and machine learning tools, which are needed to solve the most important challenges in human health. 

Researchers at Ó³»­´«Ã½ are using these new models to, for example, predict the clinical effect of mutations in the human genome, illuminate evolutionary history, and simulate the inner workings of the cell.

AI is also increasing the efficiency and productivity of engineers and computer scientists by automating many parts of coding and tool development.

Ó³»­´«Ã½ is training the next generation of researchers to be fluently bilingual in the languages of biology and AI, which is critical for advancing AI in biomedicine.