PROPOSED AI APPLICATION

Drug research evidence ranker

Research teams cannot prioritize relevant evidence

Pharmaceutical manufacturersSpecialist evaluationProposed solution scope

The problem and the proposed outcome

Application: Pharmaceutical manufacturers — Drug research evidence ranker
Problem to solve: Research teams cannot prioritize relevant evidence
Target buyer: Manufacturing administrators, qualified quality teams
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether users find relevant approved options with less search effort.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Semantic recommendation and candidate ranking
Method code: M
AI contribution: Use embeddings, learned ranking or feedback models to shortlist approved options against explicitly stated requirements; show reasons and let users choose.
Output artifact: Evidence-backed shortlist with explanation and user feedback.
Core modules: Approved options / preferences → semantic ranking → reasoned shortlist → user selection
Simple baseline: Catalogue filters, keyword matching and manual shortlisting.
Why test AI: Could improve relevance when requirements are semantic or feedback-rich; simple filters may be sufficient for small catalogues.

Pilot track: Specialist evaluation
Required data: Licensed research; authorized experimental data
Data readiness: An approved option catalogue, lawful stated preferences or requirements, representative relevance judgements and feedback are needed.
First development step: Define permitted options and relevance criteria, collect reviewed query-to-option examples and compare catalogue filters.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Manufacturing administrators, qualified quality teams: show a recent case where research teams cannot prioritize relevant evidence, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Researcher-reviewed relevance
Model evaluation: Measure precision or ranking relevance at the shortlist size, user-rated usefulness, coverage and acceptance; inspect consequential errors.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: Yes
Human review boundary: Qualified domain specialist reviews evidence, interpretation and any consequential action. Start with controlled offline or shadow evaluation; no autonomous clinical, financial, employment, industrial or safety decision.
Domain review: Use consented and authorized records; qualified practitioners review care-related outputs. Research or clinical assistance needs a separate validation pathway before field use.
Related micro-ERP catalogue IDs: IWA-B-C44C7A17BC49; IWA-B-14FADE2606E4; IWA-B-931864D69161
Integration status: Potential segment link only; no product integration implemented.