Healthcare

Discovering and identifying anomalies to facilitate early diagnostics using neural networks

Imagery analysis to help reduce misdiagnosis and medical errors

Semantic and instance segmentation to detect diseases using MRI images

Data cleaning of medical sensors, pre-processing, and signal smoothing

Using edge computing for pattern recognition and anomaly detection

Using neural networks to improve model generalization

Building predictive models based on recognized patterns

Analyzing and creating predictive algorithms and models to deliver actionable insights and support clinical decision-making

Using machine learning and data augmentation to discover patterns, predict drug resistance as well as diagnosis, epidemics, etc.

Using deep learning and machine learning on vast amounts of data to improve prediction for non-linear effects

Combining neural networks with statistical features to improve prediction quality

Developing turnkey solutions that guarantee the quality assurance of medical procedures

Digitally connecting patients with healthcare providers

Using IoT and wearables to provide remote health monitoring, diagnostics, and care access

Chronic disease management

Practice management software integration

Supporting the service quality of patients with forms and flows