Summary
Exploratory literature search and regulatory-grade systematic review serve very different purposes but are increasingly powered by the same AI technology.
In regulated environments, evidence must be reproducible, traceable, and defensible under scrutiny. Black-box AI automation falls short when high precision, governance, and auditability are required.
This session examines the structural components and best practices that separate exploratory AI use from regulator-ready evidence generation: terminology-powered article retrieval logic, explainability in article screening and data extraction, transparent performance, and human review and governance.
Join us to see how we deliver >90% accuracy in literature review under 30 minutes.
Webinar objectives
Understand why many AI literature review tools fail to meet regulatory standards and how exploratory search differs from evidence generation for HTA or regulatory submission.
Identify the risks of “black box” AI in systematic literature review, including missed studies, excess noise, and lack of transparency.
Learn what it takes to deliver clinical-grade AI for systematic literature review, including validated screening and extraction, transparent retrieval methods, and human oversight.
Featured speaker(s)
Jingcheng Du, PhD
VP, Life Science Solutions
IMO Health
Joseph Zabinski, PhD, MEM
SVP, Product Management
IMO Health
