Sr. Medical and Scientific Advisor IQVIA Laboratories Ithaca, New York
This talk clarifies the often-misunderstood differences between coding, automation, and artificial intelligence within bioanalytical laboratories, and explains why these distinctions matter as study complexity grows. Using real workflow examples, the presentation will show how coding delivers deterministic consistency, how automation scales scripted logic into reliable physical execution, and how AI fills the remaining gap by addressing dynamic, cross system variability that neither scripts nor robots can accommodate.
Attendees will learn why labs run into bottlenecks when these technologies are blended without clear purpose. The presentation will highlight practical applications such as predictive instrument monitoring, smart scheduling, and deep research synthesis—areas where AI adds unique value without compromising regulatory compliance.
The talk concludes with a forward-looking perspective on the evolving operational landscape for bioanalytical labs, outlining how coding, automation, and AI form complementary pillars that enable analysts to spend less time reacting and more time reviewing, interpreting, and innovating.
Learning Objectives:
Tell coding, automation, and AI apart and know when each one is the wrong choice.
Read the industry data critically, not just the headlines.
Apply a decision framework to real bioanalytical problems.
