Artificial Intelligence and ML-Driven Personalized Hearing Aid Manufacturing: A Control-Mapped Governance Framework

Abstract

Hearing aid manufacturing has shifted from mass-produced, one-size-fits-many devices toward highly personalized products shaped by digital ear-canal scanning, generative design, additive manufacturing, and machine-learning-tuned digital signal processing. While this shift improves acoustic fit and patient outcomes, it introduces new governance challenges: personalization pipelines depend on AI/ML components whose behavior must remain traceable, verifiable, and compliant with medical-device quality regulations across every stage of production. This paper proposes a control-mapped governance framework that links each stage of AI/ML-driven personalized hearing aid manufacturing — digital impression capture, generative shell design, additive manufacturing, acoustic personalization, quality release, and post-market monitoring — to specific governance controls and applicable standards, including ISO 13485, IEC 60118, IEC 62366, and relevant FDA quality-system regulations. We argue that treating AI/ML governance as a set of controls mapped explicitly onto manufacturing stages, rather than as a separate compliance overlay, allows manufacturers to maintain both innovation velocity and regulatory defensibility. The framework is illustrated through a stage-by-stage control-mapping table and a discussion of algorithm change control, design verification, and post-market surveillance specific to personalized hearing devices. We conclude by examining implementation challenges, including data provenance across scanning devices, validation of generative design outputs against anatomical safety constraints, and the auditability of continuously updated fitting algorithms, and we outline directions for future work on standardized AI/ML governance in personalized medical-device manufacturing.