Background: Unstructured clinical text remains a major barrier to interoperable data reuse and large-scale secondary analysis in health care. Large language models (LLMs) have the potential to ...

The examples/ directory includes sample conversations and an interactive viewer with dashboards, a cluster board, timeline, and force-directed graph.

Extract data and apply schemas across your multi-modal content, with confidence scoring and user validation enabling greater speed of data ingestion. Process claims, invoices, contracts and other ...

Medical free texts such as pathology reports contain valuable clinical data but are challenging to structure at scale. Traditional natural language processing approaches require extensive annotated ...

What if you could turn chaotic, unstructured text into clean, actionable data in seconds? Better Stack walks through how Google’s Lang Extract, an open source Python library, achieves just that by ...

Some of the most important battles in tech are the ones nobody talks about. One of them? The war against unstructured text chaos. If you’ve ever tried to extract clean, usable data from a pile of ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. Enterprises are facing key challenges in harnessing their unstructured data so they can make ...

Box announced the launch of Box Extract, which intelligently pulls information from content and saves it as metadata, helping organizations automate workflows and accelerate decision-making by making ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results. In ...

Background: Preventive cardiology relies on a comprehensive view of patient health, including biomarkers and imaging findings. However, critical data, such as coronary calcium scores (CCS) and ...

Background: Global clinical trials collect extensive unstructured medical records that richly describe participants’ clinical presentation, but their narrative format precludes quantitative analysis.