Computer models and artificial intelligence increase the fidelity and efficiency of the in vitro models for hearing loss

Hearing loss affects millions worldwide and is driven by diverse etiologies, including genetic mutations, ototoxic drug exposure, noise trauma, and aging. To investigate underlying mechanisms and test potential therapies, in vitro models, such as immortalized auditory hair cell lines, cochlear explants, and inner ear organoids, have become indispensable. However, these models face limitations in physiological relevance, scalability, and reproducibility. Recent advances in computer modeling, machine learning (ML), and deep learning (DL) offer powerful tools to enhance the accuracy, efficiency, and translational potential of these systems. This review explores the current state of integration of artificial intelligence algorithms into in vitro auditory models, highlighting its applications in high-throughput image analysis, predictive modeling of ototoxicity, optimization of culture conditions, and organoid development. Furthermore, AI-enabled tools for analyzing omics data, segmenting cochlear structures, and modeling genetic forms of deafness are presented. Despite promising developments, challenges persist, including data standardization, biological complexity, and model interpretability. Addressing these issues through improved datasets, explainable AI, and clinical integration will be key to harnessing AI's full potential for advancing auditory research and precision medicine.