Built by Oncologists, for Oncologists

The Problem

As practicing oncologists who publish research, we kept hitting the same wall: every clinical question meant months of manual chart review.

Did this regimen work for Stage IIIB patients? How did DVH parameters correlate with toxicity?

Simple questions. Months to answer. Because hospital EMRs can’t answer “show me all EGFR+ adenocarcinoma patients who received concurrent chemoradiation” without someone reviewing hundreds of charts manually.

The stakes: Without queryable data, oncologists can’t learn from their own outcomes. Can’t improve protocols. Can’t practice truly evidence-based medicine. Every department is reinventing the wheel because insights from their own patients remain locked in charts.

The Breakthrough: When AI Changed Everything

Large language models changed the equation completely.

What if AI could read pathology reports, imaging studies, and consultation notes—and automatically extract structured data?

Not generic text extraction. Real oncology understanding: recognizing that “moderately differentiated squamous cell carcinoma with extracapsular extension” will have staging implications. That this biomarker profile suggests this treatment approach. That these parameters matter for outcomes analysis.

Here’s why this is extraordinarily difficult:

Oncology data isn’t standardized. Every pathologist writes differently. Every staging system has nuances. TNM classification alone has hundreds of rules that vary by cancer type, edition, and clinical context. Getting AI to reliably extract AND interpret this—across cancer types, report formats, and clinical scenarios—requires deep domain expertise encoded into the system.

We didn’t just plug in LLMs. We built oncology-specific intelligence that understands clinical context, applies staging logic, and maps unstructured reports to structured, queryable data.

The Result

Oncologists now document care normally—dictating notes, uploading reports—while AI extracts, structures, and organizes the data automatically in the background.

What changed:

• Research-grade data capture that “nobody had time for” now happens as a byproduct of clinical documentation
• Analyses that took months now take minutes
• Questions that went unasked because data extraction was too burdensome now get answered immediately

The platform that was “too much work” became effortless. Not by asking clinicians to work differently—by making the system intelligent enough to handle the complexity.