Lifecycle answerability for artificial intelligence-enabled medical device software: a regulatory science perspective

Abstract

Artificial intelligence (AI)-enabled medical device software is increasingly expected to learn, update, explain, retrieve information, draft records, and support clinical reasoning across changing care environments. Regulatory science has responded with lifecycle-oriented tools, including software-as-a-medical-device risk categorization, quality management systems, medical device software lifecycle processes, risk management, post-market monitoring, and predetermined change control plans. These tools are necessary, but they do not by themselves specify who must respond when field experience shows that an AI-supported clinical decision, workflow, validation claim, or planned update no longer fits clinical reality. Documented deployments of sepsis early-warning software in critical care—an external validation that overturned a widely deployed model’s performance claims, the manufacturer’s subsequent model replacement, and a prospective multi-site study linking alert response latency to sepsis mortality—show that such field signals are common, consequential, and unevenly answered. We propose lifecycle answerability as a regulatory science construct for AI-enabled medical device software. It specifies standing, addressee, reason-giving, temporal trigger, and revision pathway. We provisionally define the credible field signal that triggers these obligations, differentiate the construct from established algorithmic accountability frameworks, apply it in parallel to sepsis early warning and large language model documentation, and examine what is distinctive about answerability obligations in critically ill populations. Future work should test answerability through deployment case reviews, post-market signal audits, escalation pathway simulations, and implementation studies. Lifecycle answerability complements existing lifecycle governance by specifying the institutional response architecture through which credible field signals become institutionally actionable across the software lifecycle.