arXiv:2605.07022v3 Announce Type: replace Abstract: Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage. We show that PubMed itself can be autonomously and cost-effectively turned into structured datasets that are larger, more nuanced, and more accurate than the curated databases they replace. We present three coupled contributions: (1) an LLM-based entity-tagging pipeline, grounded in nine biomedic
Advances in large language models (LLMs) and autonomous agentic systems are enabling new paradigms for data extraction and curation, making this type of automated knowledge generation feasible and impactful.
This development addresses critical bottlenecks in biomedical research by transforming lagging, expensive, and context-poor manual curation into real-time, nuanced, and scalable automated processes, accelerating discovery and application.
The fundamental method for constructing biomedical knowledge bases shifts from manual curation by experts to autonomous LLM-driven pipelines, making these resources significantly larger, more accurate, and more current.
- · Biomedical Research
- · Pharmaceutical Industry
- · AI/LLM Developers
- · Healthcare Tech
- · Manual Data Curators
- · Traditional Biomedical Database Providers
- · Research groups reliant on outdated data
Researchers gain access to vastly expanded and more accurate biomedical datasets in real-time.
This accelerates drug discovery, personalized medicine, and the development of new biotechnologies due to richer data for analysis.
The increased pace of discovery could lead to a wave of new medical interventions and a shift in the economics of biomedical R&D, potentially lowering costs and democratizing access to cutting-edge research.
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