P103: Navigating Legal Landscape for Operationalizing Site Specific Acceptance Testing of FDA Authorized AI Medical Devices
Poster Presenter
Ali Al Juboori
Medical Science Liaison
Azurity Pharmaceuticals United States
Objectives
To create a legally grounded framework for site-specific acceptance testing of FDA-authorized AI medical devices, aligned with evolving regulatory standards, and supported by practical tools that improve transparency, compliance, and legal defensibility
Method
A review of FDA-authorized AI devices shows major gaps in standardized site-specific testing. Few products include clinical, prospective, or subgroup data, and context reporting is sparse. Performance drift is common. Lacking tools and guidance, a stepwise SSAT protocol was developed
Results
The literature review confirms a significant gap between regulatory expectations and real-world practice for SSAT of AI medical devices. Despite the FDA’s call for site-specific validation, most devices are cleared based on retrospective or non-local data, with limited evidence of robust, context-specific performance evaluation. Studies show that nearly one-quarter of FDA-cleared AI devices had no performance studies at all prior to approval, and only a small fraction included prospective or randomized clinical trials. Postmarket analyses reveal that recalls and performance failures are concentrated in the first year after clearance, often linked to insufficient local validation and lack of ongoing monitoring. Furthermore, the absence of standardized tools for transparency—such as model cards and contract language—limits the ability of healthcare systems to ensure compliance and manage risk. The proposed roadmap directly addresses these gaps by providing a structured protocol for SSAT, including integration and data-fit checks, baseline performance validation, usability and cybersecurity assessments, and clear decision-making and documentation tools. This approach is designed to improve transparency, support regulatory compliance, and enhance patient safety across diverse clinical settings
Conclusion
This study demonstrates that site-specific acceptance testing (SSAT) for FDA-authorized AI medical devices can be systematically operationalized through a legally grounded, regulatorily aligned framework. By translating high-level FDA expectations—including those in PCCPs, QMSR/ISO 13485, and §524B cybersecurity requirements—into concrete testing steps and documentation tools, the framework addresses a key gap between federal authorization and safe, compliant real-world use.
The results indicate that structured SSAT is not only feasible but essential for mitigating performance variability across clinical sites, strengthening transparency, and reducing legal and operational risk for both manufacturers and healthcare organizations. The practical tools developed—contract clauses, an AI Model Card template, and standardized SSAT documentation—further enable consistent deployment practices that support audit readiness and evidence-based decision-making.
Overall, the study underscores that robust local validation is a critical component of trustworthy AI-enabled healthcare. Implementing this framework can help harmonize expectations between regulators, developers, and clinical adopters, ultimately improving patient safety, supporting sustainable post-market oversight, and enhancing the legal defensibility of AI device deployment in diverse clinical environments.
