Clinical and translational mode of single-cell measurements: clinical artificial intelligent single-cell

Abstract

With rapid development and maturity of single-cell measurements, single-cell biology and pathology has become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single-cell measurements for clinical practice, translate the signals of single-cell systems biology into determination of clinical phenotype, and predict patient response to therapies. This paper proposes a new system coined as the clinical artificial intelligent single-cell (caiSC) with the dynamic generator of clinical single-cell informatics, artificial intelligent analyzers, molecular multimodal reference boxes, clinical inputs and outputs, and AI-based computerization. This system provides reliable and rapid information for impacting clinical diagnoses, monitoring, and prediction of the disease at the single-cell level. The caiSC represents an important step and milestone to translate the single-cell measurement into clinical application, assist clinicians’ decision-making, and improve the quality of medical services. There is increasing evidence to support the possibility of the caiSC proposal, since the corresponding biotechnologies associated with caiSCs are rapidly developed. Therefore, we call the special attention and efforts from various scientists and clinicians on the caiSCs and believe that the appearance of the caiSCs can shed light on the future of clinical molecular medicine.

Keywords

• artificial intelligence
• gene sequencing
• medicine
• multi-omics
• single-cell biology
• clinical artificial intelligent single-cell