Modern healthcare generates vast amounts of data, e.g. long time series or high-resolution medical images. Artificial Intelligence is known to outperform physicians in such arduous tasks. We address all project aspects, from state-of-the-art research review, through model stability up to optimized model deployment on a custom mobile device or in a cloud.
Deep learning is part of a broader family of artificial intelligence methods based on artificial neural networks with representation learning. It is loosely based on the way neurons connect to one another to process information in animal brains. By analyzing how data is filtered through a network’s layers and how the layers interact with one another, a Deep Learning algorithm can ‘learn’ to make correlations and connections in the data.
These capabilities make Deep Learning algorithms innovative tools with the potential to change healthcare. Some examples of applications are medical image analysis, drug discovery, toxicology, and bioinformatics.
Data mining is the process of sifting through large datasets in search of patterns and valuable information. Data mining serves as a foundation for artificial intelligence. It employs various methods of statistical analysis and uses machine learning techniques to turn massive amounts of data into meaningful insights.
Today, the healthcare industry is responsible for producing about 30% of all global data, and by 2025, this will reach 36%. The ability to make sense of that segmented data can give any medical organization a major strategic advantage.
Some examples of applications are enhanced clinical decision-making, increased diagnosis accuracy, improved treatment efficiency, avoiding harmful drug and food interactions, and enabling predictive analysis.