How do you screen thousands of abstracts with transparency, reproducibility, and submission-ready outputs in a fraction of the time?
In this case study, MadeAi partnered with a leading pharmaceutical HEOR team to design and validate an AI-powered pipeline for systematic literature review. Using a structured, PICO-based framework, the team screened thousands of records across multiple databases and generated clear, traceable inclusion and exclusion decisions aligned with publication-grade standards.
The approach combined explainable AI screening with double-blind SME validation and automated data extraction, ensuring every output was reproducible, auditable, and ready for regulatory or journal submission.
See how AI-driven screening, SME validation, and research-ready outputs came together to enable faster, reliable evidence generation.
