Synthetic Datasets and Medical Artificial Intelligence Specifics

Abstract

AbstractArtificial intelligence is widely used in healthcare. World leading scientific schools are experimenting with various machine learning models and trying to implement them into medical practice. In various fields of medicine, there are good solutions already exists that allow artificial intelligence products use as medical devices for making medical decisions. For example, artificial intelligence products received a special impetus to this during COVID-19 in radiology. For high-quality artificial intelligence training, high-quality datasets are needed. That’s why as in general artificial intelligence, the transition from a model-centric approach to a data-centric approach is gradually moving. The data quality for neural networks training is becoming more and more acute. There are many problems associated with the statistically reliable medical datasets collection. The article considers an innovative solution of recent years - synthetic datasets, which can solve many dataset quality problems for artificial intelligence training. Key factors affecting the complexity of medical data mining are analyzed and examples of synthetic datasets can reduce artificial intelligence development time, balance data and improve the quality of artificial intelligence products are presented.KeywordsArtificial intelligenceSynthetic datasetsMedical artificial intelligence products