How can pharma companies use AI to extract insights from clinical trial documents?

Summary

• Databricks provides document intelligence that turns complex clinical and scientific documents (PDFs with tables, figures, and diagrams) into structured, queryable data.
• ai_parse_document, a built-in SQL AI function, captures layout, parsed tables, bounding boxes, figures, and images with captions as structured information.
• Complementary SQL AI functions work together: ai_extract pulls structured entities from unstructured text, ai_classify labels content, ai_summarize generates summaries, and ai_query gives direct LLM access.
• Agent Bricks orchestrates extraction, classification, and summarization agents, and AI Search indexes every parsed element for multimodal retrieval-augmented generation (RAG) across text and visuals.
• Unity Catalog governs the data and AI assets; ai_extract and ai_classify are available by default in workspaces with the compliance security profile enabled, supporting regulated data.

How can pharma companies use AI to extract insights from clinical trial documents?

Clinical development generates large volumes of unstructured documents, protocols, clinical study reports, case report forms, regulatory submissions, and scientific literature, that hold insights locked in tables, figures, and free text. Databricks lets pharmaceutical and life sciences teams parse those documents into structured data and then extract, classify, summarize, and search across them with AI, all in one governed platform. This turns document backlogs into queryable data that scientists and analysts can work with directly.

Why Databricks for clinical trial document intelligence

• Parse complex documents into structured data. ai_parse_document is a SQL AI function that transforms complex documents into structured, queryable data, capturing layout information, parsed tables, bounding boxes, figures, and images with captions, not just extracted text.
• Extract, classify, and summarize in SQL. Complementary AI functions run directly in SQL: ai_extract pulls structured entities from unstructured text, ai_classify classifies content against custom labels, ai_summarize generates summaries, and ai_query provides direct large language model (LLM) access.
• Orchestrate with Agent Bricks. Declarative agents optimize extraction, classification, and summarization with natural language, and a Supervisor Agent coordinates document-analysis agents with other specialized agents for multi-step workflows.
• Ask questions across text and visuals. AI Search indexes every parsed element for multimodal RAG applications that understand both text and figures, so teams can query document content in natural language.
• Process documents continuously. Spark Declarative Pipelines handle automatic, incremental processing of new documents as they arrive, and the same parsed data feeds AI/BI Dashboards for analysis.
• Govern regulated data. Unity Catalog provides governance and observability across the data and AI assets; ai_extract and ai_classify are available by default in workspaces with the compliance security profile enabled, which supports working with sensitive, regulated data.

Getting started

• Read the announcement: PDFs to production: announcing state-of-the-art document intelligence on Databricks.
• Start with ai_parse_document and the SQL AI functions (ai_extract, ai_classify, ai_summarize, ai_query) to parse a set of documents and extract structured fields.
• Use Agent Bricks and AI Search to build governed document-analysis agents and multimodal RAG on top of the parsed data.

FAQs

What kinds of documents can Databricks parse?

ai_parse_document handles complex documents including PDFs with tables, figures, and diagrams, capturing layout, parsed tables, bounding boxes, figures, and captioned images as structured, queryable data.

Can non-technical users get answers from clinical documents?

Yes. Once documents are parsed, AI Search enables multimodal RAG and Agent Bricks lets teams ask questions in natural language, so users can access insights without writing extraction code for each document.

How is sensitive clinical data governed?

Unity Catalog governs the data and AI assets, and ai_extract and ai_classify are available by default in workspaces with the compliance security profile enabled, supporting work with regulated data.