Automated Medical Analysis

Clinical trials require various methods for better experiment design and higher universality of results. RealMedSci can combine clinical data with multi-dimensional social and psychological indicators to better optimize the design of clinical trials. It can also screen appropriate patient cohorts and reduce the interference in the experiments for better clinical verification.

RealMedSci can participate in disease screening and prediction and evaluate human health according to behaviors, images, biochemical and other inspection results to assist in the early detection and intervention of diseases and to facilitate preventive medical care.

The massive biomedical data drives the interest in AI among the pharmaceutical industry. Deep learning can build biological process models based on measured and textual data. Together with other intelligent algorithms, deep learning is expected to greatly impact drug development. RealMedSci uses the existing data to establish an intelligent model, with which RealMedSci make inferences to support drug R&D, cut R&D costs, and increase R&D success rate.

RealMedSci can make medical teaching more immersive and practical with more feedback. RealMedSci enables the combination of theoretical medical teaching and tests with practices for better learning results. Besides, RealMedSci can also help on-the-job training, work as an automated medical scientific research platform, and carries out Internet or mobile teaching for lifelong medical education.