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#626: Navigating the Regulatory Labyrinth: How Well Do Language Models Read the Fine Print?
Wednesday June 17, 2026 1:45pm - 2:45pm EDT
107AB
Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-26-616-L04-P; CME 1.00; RN 1.00

Comprehensive benchmarking of four major LLMs across 100 FDA approval packages identifies optimal models for regulatory tasks. Study provides critical insights on accuracy, limitations, and implementation strategies for regulatory AI adoption.

Learning Objectives

Identify which LLM models demonstrated highest accuracy of generative AI for extracting critical regulatory data from FDA approval packages; Recognize key limitations and failure modes when using LLMs for regulatory intelligence, including hallucination rates and performance degradation patterns; Apply evidence-based benchmarking to select an LLM

Chair

Sam Kay, RAC

Speaker

Navigating the Regulatory Labyrinth: How Well Do Language Models Read the Fine Print?
Cameron Kieffer, PHD

Navigating the Regulatory Labyrinth: How Well Do Language Models Read the Fine Print?
Jeff MacDonald, PHARMD

Navigating the Regulatory Labyrinth: How Well Do Language Models Read the Fine Print?
Aleksandr Merenkov


Speakers
avatar for skay8

skay8

VP of Pharmaceutical Strategy, Basil Systems, United States
Sam Kay is Vice President of Pharma at Basil Systems, where he leads AI-powered regulatory intelligence solutions for global pharmaceutical companies. A recognized authority in the field, Sam co-authored Chapter 6 of Regulatory Intelligence Reimagined (RAPS) and teaches Regulatory... Read More →
CK

Cameron Kieffer

Director, Global Regulatory Intelligence and Policy Research, Takeda, United States
avatar for Jeff MacDonald

Jeff MacDonald

Associate Director, Global Regulatory Policy and Intelligence, BeOne Medicines, United States
AM

Aleksandr Merenkov

Associate Director, Global Regulatory Intelligence, Genmab US, Inc., United States
Wednesday June 17, 2026 1:45pm - 2:45pm EDT
107AB The Pennsylvania Convention Center 1101 Arch Street Philadelphia, PA 19107 USA
  03: Data-Tech-AI, Session

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