Webinar

Precision at Scale: Agentic AI Delivers Human-Accurate Biomedical Metadata to Accelerate Precision Medicine

Key Highlights

High-quality metadata is essential for large-scale biomedical analysis and AI model development.

But in public repositories like GEO, metadata is incomplete, inconsistently labeled, and scattered across GEO records, full-text publications, and supplementary PDFs.

Manual curation is slow, labor-intensive, and cannot scale. Existing automated methods offer speed, but often miss context, produce inconsistent outputs, and lack traceability.

Now you can get both - human-level metadata quality and automation at scale.

Webinar

Upcoming Webinar

In collaboration with

Precision at Scale: Agentic AI Delivers Human-Accurate Biomedical Metadata to Accelerate Precision Medicine

What the AI Co-Scientist Paper Actually Demonstrates for Biologists and Data Scientists

July 3, 2025

10:30 AM PST / 1:30 PM EST

High-quality metadata is essential for large-scale biomedical analysis and AI model development.

But in public repositories like GEO, metadata is incomplete, inconsistently labeled, and scattered across GEO records, full-text publications, and supplementary PDFs.

Manual curation is slow, labor-intensive, and cannot scale. Existing automated methods offer speed, but often miss context, produce inconsistent outputs, and lack traceability.

Now you can get both - human-level metadata quality and automation at scale.

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Real-World Applications We’ll Cover

Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.

Join us for a behind-the-scenes look at a Multi-agent AI system that achieves:

93% recall across 23 key metadata fields including tissue, disease, cell line, donor ID, and treatment.
Outperformance of GPT-4.1 single-pass prompting on accuracy, F1 score, and traceability.
Curation of 4652 samples from 78 GEO datasets in days instead of weeks.
4x reduction in manual effort equivalent to replacing a 3-person expert team working for 1 month.
Human-level accuracy, with 100% concordance on disease and 97% on gender based on CellxGene benchmarks.
Traceable records with field-level evidence attribution and confidence scores.

‍Register for our webinar to see how the Agentic AI system fits into scalable data workflows.

What You’ll Learn

4x reduction in metadata prep time
Replace multi-week manual curation with automated agents that process thousands of samples in hours.
93% recall across 23 metadata fields
Extract high-value fields including tissue, disease, donor ID, and treatment with ontology-linked outputs.
Human-level accuracy at scale
Match or exceed human curation on 78 GEO datasets, with built-in validation and consensus steps.
Deployment across public and proprietary data
Apply the system to GEO, multi-omic pipelines, and internal clinical datasets.
100% traceable metadata generation
Access field-level evidence, source attribution, and confidence scores for every output.

Check out the pre-print here

Why This Matters for Biomedical Researchers

If you’re working with complex biological data, you may be asking:

Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?

Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.

Why This Matters TO YOU

80% Faster Model Development at Scale
‍Achieve over 80% reduction in model development time through transformation of fragmented public data into structured, AI-ready assets at scale.

93% Human-Level Metadata with Traceability
‍Achieve 93% accurate, consistent, and traceable metadata at scale to enable high-confidence AI predictions and ensure regulatory defensibility.

Proprietary Value from Public Data
‍Standardize public data at scale to build proprietary models and avoid costly in-house collection.

4x Efficiency in Metadata Operations
‍Replace 120+ person-hours of expert curation with automated agents. Scale curation 4x faster without increasing headcount.

Foundation for Generative and Predictive AI
‍Support explainable models with harmonized metadata that meets clinical and regulatory requirements.

Fully Agentic, End-to-End System
‍Deploy a system of LLM-orchestrated micro-agents that extract, normalize, and validate metadata with 24/7 automation and traceability.

Modular and Extensible by Design
‍Add new schemas, ontologies, or data types with plug-in flexibility that scales with evolving R&D needs.

Meet the Expert of this discussion

Nobal Dhruw

Senior Manager - ML

Key Takeaways

How data providers ensure adherence to quality standards through validation and compliance.

How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.

Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.

Key Takeaways

Reduce operational costs by streamlining data delivery through reusable, governed products.

Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.

Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.

Equip cross-functional teams to act on trusted data—faster, and with greater confidence.

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Other Resources

All Webinars Case Studies Dataset Roundup Documentation Glossary Solution Briefs Whitepapers

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