Overview of artificial intelligence applications in auditory healthcare and scientific communication
| Domain | AI application | Details/technologies involved |
|---|---|---|
| Scientific communication | - Manuscript drafting and editing- Peer review optimization- Plagiarism detection | - ChatGPT, Claude 3, and other generative large language models (LLMs) for generating and refining scientific text- Penelope.ai and Scholarcy for formatting, compliance, and readability checks- AI-powered plagiarism detection tools (e.g., iThenticate, Turnitin AI) ensure originality |
| Clinical practice (otolaryngology) | - AI-assisted cochlear implant (CI) mapping- Diagnostic support for audiological disorders- Surgical planning and risk assessment | - Deep neural networks (DNNs) and convolutional neural networks (CNNs) for speech sound classification- AI-driven optimization of CI parameters via real-time auditory feedback- Machine learning in imaging analysis for middle ear pathology and tumor detection |
| Mobile and telehealth applications | - AI-based auditory rehabilitation- Remote monitoring and therapy- Virtual audiometry platforms | - AI-enabled mobile apps (e.g., HearCoach, Amptify) with adaptive training modules- Natural language processing (NLP) for speech feedback and assessment- Gamification strategies to promote adherence and cortical plasticity- Voice biomarker analysis for early detection of hearing decline |
| Ethical and future perspectives | - Development of ethical frameworks- AI transparency and explainability- Cross-disciplinary innovation | - Algorithmic audit systems to ensure bias minimization and fairness- Involvement of ethics boards and institutional review in AI deployment- Integration of wearable devices and brain-computer interfaces for closed-loop hearing systems- Responsible AI (RAI) frameworks for regulatory and clinical compliance |
