The "Elevate" by MAPS Podcast
This is the "Elevate" podcast by the Medical Affairs Professional Society (MAPS). MAPS is the premier non-profit global Medical Affairs organization FOR Medical Affairs professionals BY Medical Affairs professionals across all different levels of experience/specialty. Together with over 15,000+ Medical Affairs members from 280+ companies globally, MAPS is transforming the Medical Affairs profession to increase its value to patients, HCPs, industry and society. JOIN THE MOVEMENT!
The "Elevate" by MAPS Podcast
SciTech & AI Series: Smarter Summaries, Better Decisions: AI Across Medical Affairs
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Welcome to Episode 4 of the SciTech Critical Evaluation of Literature and AI Series: “Smarter Summaries, Better Decisions: AI Across Medical Affairs"
Medical Affairs teams handle a flood of scientific data, so how can AI help turn complexity into clarity? Join experts from the MAPS Scientific & Technical Knowledge Domain to discover how AI tools evaluate evidence for diverse functions, from field teams to HEOR and publications.
In this session, you’ll learn practical strategies for creating concise, decision-ready summaries and explore why context and clinical relevance must guide every AI-driven workflow. If you want to leverage AI to evaluate evidence, podcast is for you!
Learning Objectives:
- Describe how AI tools adapt evidence evaluation for different Medical Affairs functions
- Explore how field medical, medical communications, publications, medical information, and HEOR teams can prompt the same data set differently to generate outputs tailored to their specific needs.
- Demonstrate strategies to create concise, decision-ready summaries using AI
- Learn practical prompting and structuring techniques that convert complex scientific literature into clear, stakeholder-specific deliverables that support better strategic and scientific decisions.
- Explain why context and applicability are essential in AI-enabled evidence workflows
- Understand how role-specific context, intended use, and decision impact must guide both the prompting and interpretation of AI-generated outputs.
Hear more from the Scientific & Technical Knowledge Domain through their position paper: "Safeguarding Scientific Rigor in the Critical Evaluation of Literature"