Medical NLP content extraction or relationship linking is extremely complex requiring advanced expertise and presents unique data quality challenges when trying to scale. Domain experts with advanced training, tooling expertise,...
High-quality Knowledge Graphs still mostly rely on structured data curated by humans. Such reliance on human curation is a major obstacle to the creation of a comprehensive, always-up-to-date knowledge graph...
For many years, the NLP healthcare application development are driven by NLP engineers. The engineers-centred healthcare NLP unintentionally creates barriers among healthcare workers and slows the deployment of NLP in...
Healthcare organizations can face numerous challenges when developing high-quality machine learning models. Data is often noisy and unstructured, and developing successful models involves experimenting with numerous parameter configurations, datasets, and...
AI models and pipelines for text and image processing are currently used in intelligent applications on all verticals, from Healthcare to Finance and Security. Up until now, they have been...
See More