Molecular Tumor Boards (MTBs) often struggle to process more than a handful of cases per session while AI-augmented MTBs are scaling to address their entire patient population with precision. This widening gap means that without automation, institutions aren't just losing time; they are missing the "hidden" insights like -rare drug-variant matches and longitudinal patterns that define modern oncology.

The Molecular Tumor Board should be the strategic center where oncology, pathology, and genomics converge. However, at many institutions, it has become an administrative bottleneck. A single patient’s profile is a fragmented mosaic of genomic reports, longitudinal EHR narratives, and a constant stream of new clinical trial data. When highly trained clinicians spend hours manually synthesizing these dossiers, the consequences are direct: fewer patients are reviewed, treatment starts are delayed, and the risk of overlooking a key therapeutic pivot increases.

To solve this, we deploy our agentic AI workflow Polly that transforms the "pre-read" from a disorganized pile of documents into a structured, evidence-linked patient dossier. This doesn't just simplify preparation; it enables boards to address more patients with greater contextual depth incorporating imaging readouts, prior lines of therapy, and real-time trial matching exceeding the capacity of manual curation.

The Problem: The High Cost of Fragmented Data

Tumor Boards are designed for high-stakes deliberation, yet they are often bogged down by data friction. When evaluating a patient with a rare mutation or a complex treatment history, the evidence is scattered:

• The Efficiency Drain: Highly skilled oncologists and pathologists spend more than 3 hours on manual data synthesis. This inefficiency forces a "throughput wall" where boards only review the most complex 10% of cases, leaving the vast majority of patients without a precision review.
• The Cost of Delay: Manual curation often extends prep time by weeks. In advanced oncology, a 14-day delay in treatment can lead to disease progression that renders a patient ineligible for the very clinical trials the board is working to identify.
• Genomic Complexity: A specific mutation’s significance might be hidden in a recent publication or a supplementary PDF from a major conference.
• Treatment History: Past responses to immunotherapy are buried in pages of unstructured clinician narratives.
• Trial Matching: Eligibility criteria for an emerging Phase II trial may exist only in a complex clinical protocol document.

This manual synthesis is risky. Inconsistent data formats and the volume of literature increase the chance that a life-saving therapeutic option or a critical safety signal is overlooked.

Our Solution: Agentic Precision Oncology

We leverage a specialized agentic AI system to bridge the gap between raw medical records and clinical action.

Intelligent Harmonization with Polly Xtract

Instead of simple keyword searches, Polly Xtract uses a multi-agent engine to ingest heterogeneous documents from NGS reports to pathology notes. It doesn't just "read" text; it understands the clinical context.

• Entity Mapping: Automatically maps mutations, drugs, and biomarkers to standard ontologies (like MONDO or HGNC).
• High-Fidelity Extraction: Extracts treatment durations and RECIST scores from imaging reports.
• Auditability: Every extracted insight includes an inline citation, allowing a pathologist or oncologist to click and see the exact sentence or table in the source document.

Polly Atlas

All extracted data is pushed into a relational Atlas environment. This creates a "longitudinal patient twin" that integrates clinical, genomic, and trial data into a single queryable source.

What This Means for the Tumor Board

By automating the preparation phase, the MTB shifts from a review of what happened to a discussion of what to do.

The Outcomes

Implementing an agentic AI workflow transforms the economics and efficacy of precision oncology:

• Increased Throughput: Reduce preparation time by allowing boards to review more cases per week without increasing staff burnout.
• Expanded Access: By lowering the "cost per case," precision medicine becomes accessible to community hospitals, not just elite academic centers.
• Evidence-Backed Confidence: Boards move forward with a 360-degree view of the patient, supported by data that is fully traceable and regulatory-ready.

Connect with us to explore how our agentic AI workflow can streamline your clinical decision-making today.

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