Applied AI · Life sciences
From research papers to reviewable evidence.
A scientific literature workflow that gathers papers, extracts candidate findings with source quotes, and gives domain experts an explicit review step.
- Engagement
- Literature ingestion and expert review
- Work
- Implemented · In development
- Tools
- Ruby on Rails · Python · LLM integration · PostgreSQL
01 / The challenge
Evidence needs context.
Turning a paper into a useful scientific record requires more than recognizing a name. A reviewer needs the supporting passage, the candidate's relationship to existing records, and enough context to judge what the paper actually studied.
02 / The contribution
From paper to expert review.
- Built scheduled literature collection, deduplication, and a searchable queue for reviewing incoming references.
- Implemented staged AI classification and extraction with source packages, quote validation, and visible processing states.
- Created a review workflow for confirming matches or recording new candidates, with a named reviewer, required rationale, and an append-only decision record.
03 / The result
A decision with a source behind it.
An implemented path from source material to an inspectable candidate and a recorded expert decision. Literature collection is integrated into the main application; the AI review workflow is being developed and calibrated with scientific review.
AI extraction and review remain in development. Scientific acceptance depends on expert review and source evidence.