About the Workshop
Artificial intelligence (AI) is making a big impact on patient experiences, clinician workflows, researchers, and the pharmaceutical industry work in the healthcare sector. In recent decades, technological advancements across scientific and medical disciplines have led to a torrent of diverse, large-scale biomedical datasets such as health, imaging data, clinical notes, lab test results, and other ‘omics data. The dropping costs of genomic sequencing coupled with advances in computing allow unprecedented opportunities to understand the effects of genetics on human disease etiologies and has resulted in the creation of population-level biobanks. As a consequence, the demand for novel computational methods, computational infrastructure, and algorithm improvements to efficiently process and derive insights from these datasets, particularly where it applies to clinical translational research, has dramatically increased. In addition to handling the sheer size and quantity of biomedical data, newly developed methods must also adapt and employ state-of-the-art AI algorithms that account for the unique complexities of biomedical datasets, such as sparseness, incompleteness, and noisiness of data, data multidimensionality such as clinical measurements from electronic health records, prescription drug data, environmental exposures. Additionally, these methods have to leverage the advances in high-performance computing like GPUs, faster inter-connects, and fast-access memory to help generate the needed insights at a faster rate.
In this workshop, we have invited leading experts to share their viewpoints on the development and application of artificial intelligence and cutting-edge computing approaches that are driving innovation in precision medicine. We will discuss current breakthroughs in which our speakers are involved and the strengths and limitations of artificial intelligence in medicine.