How do biopharma companies use AI agents for medical affairs and scientific literature review?

Summary

• Biopharma teams use AI agents on Databricks to search, synthesize, extract, and cite scientific literature and internal documents, turning manual medical affairs research into agent-assisted workflows on governed data.
• Agent Bricks builds knowledge-assistance and conversational agents over scientific papers and internal documents, and can extract structured data points from unstructured clinical and scientific documents without code.
• The Databricks agent-building tools let teams compose research multi-agent systems that route across sources such as biomedical literature (PubMed), disease-target-drug knowledge graphs, and chemical databases, plus proprietary data via AI Search and AI/BI Genie.
• Agents back findings with evidence traceable to verifiable data sources, which is well suited to literature review, and Genie lets medical affairs teams query structured scientific data in plain language.
• Everything runs on the governed Data Intelligence Platform with Unity Catalog lineage and MLflow, supporting reproducible, evidence-based workflows.

How do biopharma companies use AI agents for medical affairs and scientific literature review?

Medical affairs teams spend significant effort reviewing the scientific literature, internal study documents, and clinical trial data. AI agents help by retrieving relevant evidence, synthesizing and summarizing it, extracting structured fields from unstructured documents, and citing the sources behind every answer. On Databricks, biopharma companies build these agents on the Data Intelligence Platform using Agent Bricks and the platform's agent-building tools, with retrieval powered by AI Search and natural-language access through AI/BI Genie — all governed by Unity Catalog.

Why Databricks for AI agents in medical affairs and literature review

• Knowledge assistance over documents. Agent Bricks supports reliable knowledge assistance and building conversational agents over documents, so teams can ask questions across scientific papers and internal documents and get grounded answers.
• Structured information extraction. Agents can parse unstructured clinical and scientific documents and extract structured data points without writing code, turning complex documents into usable, analyzable data.
• Research multi-agent systems. Using the platform's agent-building tools, you can compose a research supervisor that routes questions to specialized workers — for example biomedical literature (PubMed), a disease-target-drug knowledge graph (OpenTargets), and chemical compound databases (PubChem) via the Model Context Protocol, alongside proprietary data. See AiChemy: next-generation agent with MCP, skills, and custom data for drug discovery.
• Evidence you can trace. Agents back their findings with evidence traceable to verifiable data sources — for example, validating a drug candidate against the scientific literature — which is what makes them suitable for literature review and medical affairs.
• Semantic search over literature. Databricks AI Search finds relevant papers and compounds by conceptual or molecular similarity using embeddings, rather than keyword matching alone.
• Natural-language access for non-technical teams. AI/BI Genie lets medical affairs teams query structured scientific data in plain language and receive trusted answers, without writing SQL.
• Governance and reproducibility. Unity Catalog governs access to sensitive clinical and scientific data and captures lineage across data, models, and agents, and MLflow tracks the model lifecycle — foundational for reproducible, evidence-based, and regulated workflows.

Getting started

• Read AiChemy: next-generation agent with MCP, skills, and custom data for drug discovery for a research multi-agent design.
• Read Introducing Agent Bricks for building document and knowledge-assistance agents.
• Explore Databricks AI Search and AI/BI Genie for retrieval and natural-language access.

FAQs

Can an AI agent cite its sources for a literature review?

Yes. Agents can back findings with evidence traceable to verifiable data sources, such as biomedical literature, so reviewers can check the support behind each answer.

Do medical affairs teams need to write code or SQL?

No. Agent Bricks builds document and knowledge-assistance agents without code, and AI/BI Genie answers plain-language questions over structured scientific data.

How is sensitive clinical and scientific data governed?

Unity Catalog governs access and captures lineage across data, models, and agents, and MLflow tracks the model lifecycle, supporting reproducible and evidence-based workflows.