Robustness tests for biomedical foundation models should tailor to specification

2025-02-14

AI Summary

Current biomedical AI regulatory frameworks emphasize model robustness but lack actionable implementation guidelines—particularly for foundation models, which exhibit broad capabilities yet remain highly susceptible to distributional shifts; conventional testing methods struggle to balance feasibility and effectiveness. This paper proposes a task-oriented, priority-driven, customizable robustness evaluation framework that dynamically adapts testing objectives to predefined regulatory specifications. Its key contribution is a specification-aware, fine-grained robustness taxonomy that standardizes risk categories and precisely aligns them with test objectives. By decoupling robustness dimensions, modeling distributional shift scenarios, and mapping tests to regulatory compliance requirements, the framework establishes a reproducible, verifiable, and auditable testing paradigm. This enables synergistic optimization of technical development and risk mitigation in regulated biomedical AI deployment. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Existing regulatory frameworks for biomedical AI include robustness as a key component but lack detailed implementational guidance. The recent rise of biomedical foundation models creates new hurdles in testing and certification given their broad capabilities and susceptibility to complex distribution shifts. To balance test feasibility and effectiveness, we suggest a priority-based, task-oriented approach to tailor robustness evaluation objectives to a predefined specification. We urge concrete policies to adopt a granular categorization of robustness concepts in the specification. Our approach promotes the standardization of risk assessment and monitoring, which guides technical developments and mitigation efforts.

Problem

• tailored robustness tests
• biomedical foundation models
• regulatory framework guidance

Innovation

• Priority-based testing approach
• Task-oriented robustness evaluation
• Granular robustness categorization