Artificial Intelligence Guided Precision Medicine Approach To Hematological Disease

Background: Next-generation sequencing (NGS) is an attractive tool for prospective use in the field of precision medicine. Using NGS to guide therapy has provided a large volume of genomic data and therapeutic actionability of somatic NGS results. These data are evolving too rapidly to rely solely on human curation. So the interpretation of the clinical significance of such large amounts of genetic data remains the most severe bottleneck preventing the realization of precision medicine. Watson for Genomics (WfG) is a representative artificial intelligence (AI) software, which analyzes and categorizes genetic alterations that are related to disease progression and provides a list of potential therapeutic options within 3 minutes per sample. Recent reports suggested that WfG could empower tumor boards and improve patient care by providing a rapid, comprehensive approach for data analysis and consideration of the up-to-date availability of clinical trials (Patel NM, et.al. Oncologist. 2018). However, only limited data are available regarding the utility of AI-guided precision medicine approach in the field of hematological disease. The purpose of this study is to test the utility of AI in assisting the interpretation of high throughput genomic data from patients with the hematological disease.