This research introduces a structured approach for effectively deploying large AI models in high-stakes vertical domains where performance gaps are most concerning.

• Identifies key limitations of current AI models in critical sectors, including brittleness, contextual misalignment, and trust issues
• Proposes a comprehensive framework for situating, evaluating, and adapting AI models to domain-specific requirements
• Addresses vertical-specific challenges across healthcare, education, and legal domains
• Focuses on practical implementation rather than benchmark performance alone

For healthcare applications, this framework is crucial as it directly addresses the unique challenges of medical AI deployment—where confident but incorrect outputs could negatively impact patient care, and where domain-specific context is essential for safety and efficacy.

A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models