Life-science teams are under pressure to deliver systematic literature reviews (SLRs) and dossier content faster, with higher consistency and full traceability. Publication volumes keep growing, templates multiply, and experts lose time on repetitive screening, extraction, and first-pass drafting.
Human-in-the-lead AI is the practical model: experts define protocols, inclusion/exclusion criteria, variables, and dossier strategy. AI accelerates the tedious work, while accountability stays with the human reviewer.
What Changes in Practice
1) SLR screening becomes dramatically faster
In the session, Willie shared a concrete benchmark:
- 5,000 abstracts
- Two human reviewers: ~2 weeks
- AI-assisted: ~half a day (plus upfront alignment + QC sampling)
Quality depends on the protocol. If criteria are vague, outputs will be vague, just faster.
2) Full-text extraction scales, if the system can handle reality
Full-text review requires reading text, tables, and graphs across messy PDF formats. Extraction can explode quickly: 200 PDFs × 150 variables = 30,000 fields. A usable AI must reliably say “not reported” instead of inventing values.
3) Dossier authoring becomes modular and reusable
Dossiers aren’t “one prompt.” They’re structured sections with different requirements. In OneRay.ai, each section has:
- tailored instructions
- mapped sources
- versioning and final selection
- export into the final dossier structure
AI can draft first passes (often targeting ~75–85%), but expert iteration is still required for submission-ready quality.
Why OneRay.ai’s “traceable AI” matters
Experts don’t accept outputs without provenance. OneRay.ai emphasizes:
- sentence-level source referencing
- visible AI rationale
So reviewers can inspect why something was included and how conclusions were formed. That’s how you keep work inspection-ready.
The part leaders forget: operating model > tool
The session was blunt: AI implementation is change management. Start with a measurable, repetitive use case, establish QC and baselines, and then scale across indications and markets. Trying to “solve the biggest problem first” usually just creates an expensive pilot that nobody trusts.