• MRIDUL PRATIK KHAKHA

Artificial Intelligence (AI) is emerging as a supportive technology in audiology and has the potential to improve hearing screening, assessment, early identification, and follow-up of hearing difficulties. Early identification of hearing difficulties is particularly important for children because hearing access can influence communication, speech and language development, learning, social interaction, and participation in educational activities. AI-assisted systems can support audiologists by processing large amounts of hearing-related data, recognizing patterns in auditory signals, assisting with screening results, and organizing information for monitoring and follow-up. AI applications may be explored in areas such as otoacoustic emissions (OAE), auditory brainstem response (ABR), audiological data analysis, speech and voice analysis, and risk-based screening. These technologies can potentially improve the efficiency and accessibility of hearing screening, particularly when combined with appropriate professional supervision. However, AI-generated results should not be considered a substitute for comprehensive audiological assessment or professional clinical judgment. Accuracy, data quality, algorithmic bias, privacy, accessibility, and ethical use are important considerations in the implementation of AI-based hearing technologies. Audiologists remain essential for interpreting assessment findings, confirming clinical decisions, communicating results to families, and recommending appropriate intervention and educational support. When responsibly integrated with professional expertise, AI can contribute to earlier identification of hearing difficulties and support timely, individualized, and inclusive educational opportunities for children.

Artificial Intelligence (AI), AI-Assisted Hearing Assessment, Audiology, Hearing Difficulties, Hearing Loss, Early Identification, Early Detection, Hearing Screening, Children with Hearing Impairment, Audiological Assessment, Otoacoustic Emissions (OAE), Auditory Brainstem Response (ABR), Machine Learning, Pattern Recognition, Speech Analysis, Voice Analysis, Auditory Signals, Risk Prediction, Data Analysis, Clinical Decision Support, Hearing Technology, Assistive Technology, Hearing Aids, Cochlear Implants, Communication Support, Speech and Language Development, Inclusive Education, Early Intervention, Personalized Support, Progress Monitoring, Audiologist, Professional Supervision, Digital Health, Accessibility, Data Privacy, Ethical AI, Inclusive Learning, Educational Support, Multidisciplinary Collaboration, Child-Centred Assessment.

"AI-Assisted Hearing Assessment and Early Identification of Hearing Difficulties", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.a883-a889, October-2026, Available :http://www.jetir.org/papers/JETIR2610089.pdf

"AI-Assisted Hearing Assessment and Early Identification of Hearing Difficulties", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppa883-a889, October-2026, Available at : http://www.jetir.org/papers/JETIR2610089.pdf