AI in systematic literature reviews How Life Science companies can seize the opportunity and manage risk

Systematic literature reviews (SLRs) play a critical role in evidence generation, regulatory submissions, clinical development and strategic decision-making within life science companies.

As AI-driven tools rapidly enter the research workflow, they offer the promise of faster screening, smarter synthesis and improved efficiency. At the same time, they raise important questions around bias, transparency, reproducibility, and regulatory defensibility.

In this 45-minute webinar, two expert speakers will explore how life science researchers can integrate AI into systematic review processes without compromising scientific rigor.

Agenda highlights include:

• Emerging issues and pitfalls in AI-enabled SLRs
  - Where AI is transforming (and disrupting) traditional workflows
  - Risks related to bias, hallucinations and black-box methodologies
  - Transparency, reproducibility and auditability concerns
  - Regulatory and compliance considerations for life science organizations
  - Practical governance guardrails for responsible AI adoption
  
  
• Practical examples: Conducting SLRs in the age of AI
  - Where AI fits into the end-to-end SLR workflow
  - Enhancing search strategy development and screening efficiency
  - Human-in-the-loop best practices for validation and quality control
  - Real-world examples of AI-supported evidence synthesis
  - Ensuring outputs meet scientific and regulatory standards
  
  This session is designed for researchers, evidence-generation teams, medical affairs professionals and information specialists in life science companies. Join our experts to explore a balanced and actionable framework for leveraging AI in systematic literature reviews — combining innovation with methodological integrity.