Neusoft RealMedSci Automated Medical Analysis Platform uses big data, machine learning, deep learning, and text analysis technologies to provide doctors with AI tools which can help doctors integrate data flexibly and efficiently in medical scientific researches and explorations. This platform offers automated analysis and engine templates targeted for the etiology, diagnostic, treatment, and prognosis researches to assist medical institutions in producing high-efficient and high-quality academic research results.
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.
Handle differentiated medical data by standardizing and aggregating multi-source heterogeneous medical data; clean dirty medical data to manage data quality; solve problems on medical data quality to prepare high-nutrition raw materials for the R&D of automated medical scientific research applications.
Well-designed data annotation tools greatly improve the efficiency and reduce the workload. The integrated automated knowledge service technology can do its best to assist in the medical data annotation.
Use the “one-stop” AI platform to build an all-round stack that supports multi-statistical analysis methods, algorithm selection, algorithm training, algorithm optimization, model verification, and application deployment. Use Solve the problem of algorithm selection and reference. Offer a reliable tool to enable the AI R&D capacity of personnel in the medical industry and institutions and medical scientific researchers and to solve the shortages of medical scientific researchers and cross-domain application experts.