This proposal aims to leverage a large-scale, anonymized patient database to evaluate the real-world outcomes of hearing aid use through advanced artificial intelligence (AI) and machine learning techniques. By analyzing vast and diverse datasets—encompassing audiometric data, patient-reported outcome measures (PROMs), hearing aid settings, and demographic information—we will develop predictive models to identify the key determinants of successful hearing aid adoption and satisfaction. The core of this initiative is to move beyond traditional, one-size-fits-all approaches and towards a data-driven, personalized framework for audiological care. The AI will uncover complex patterns and correlations that are not apparent through conventional analysis, providing clinicians with powerful tools to optimize hearing aid fittings and counseling from the outset.