Cancer registries are the backbone of oncology research, yet they are traditionally plagued by manual bottlenecks, reporting delays, and data that is often outdated by the time it reaches researchers.

This keynote unveils industry-first benchmarks proving that AI-powered abstraction has reached the milestone of regulatory-grade accuracy. We share performance metrics demonstrating that the combination of healthcare-specific LLMs, specialized clinical NLP, and a deep encoding of the 2,500+ page long SEER and NAACCR guidelines now meets or exceeds the accuracy of a certified Oncology Data Specialist (ODS, formerly CTR).

We will present comparative results across the most common solid tumors – including breast, lung, and colorectal cancers – proving that automated abstraction is no longer just a speed multiplier, but a superior, high-fidelity alternative to traditional manual processes.

Moreover, regulatory-grade AI is defined by more than just the accuracy of its output; it is defined by the rigor of its process. We explain how Patient Journey Intelligence, the industry’s first FDA-ready secondary use platform, moves beyond the “what” (accurate results) to the “how.”

We will demonstrate how every extracted clinical fact is supported by end-to-end provenance, deterministic versioning, and a complete audit trail that traces every data point back to its original location in the source multimodal data.

Attendees will learn how to transition from “black-box” automation to a transparent, governed infrastructure that meets the FDA’s newest requirements for Real-World Evidence (RWE).