Artificial intelligence (AI) has stimulated extensive research in clinical neurology, but relatively few AI systems have meaningfully changed bedside decision-making. This translational gap is not explained solely by inadequate algorithms....
No actionable change for audiologists specifically; this neurology-focused review highlights systemic barriers to AI adoption that are broadly relevant but do not provide audiology-specific clinical guidance.
Understanding why AI fails to reach the bedside in a related field like neurology offers a cautionary and instructive framework for audiology researchers and companies investing in AI-driven hearing diagnostics and fitting tools.
- 01Few AI algorithms developed for clinical neurology have successfully moved into routine bedside use.
- 02The review identifies a translational gap between AI research performance and real-world clinical deployment.
- 03Key barriers include workflow integration, regulatory hurdles, and lack of prospective clinical validation.
- 04Lessons from neurology's AI experience are directly transferable to audiology AI development.
- 05Published in Journal of Clinical Neurology; not audiology-specific but thematically relevant.
Few AI algorithms developed for clinical neurology have translated into actionable bedside decision-making.
opinionpartially supported- PMID
- 42421400
- DOI
- 10.3988/jcn.2026.0312.
- Journal
- Journal of Clinical Neurology
- Publication type
- review
- Evidence level
- 5
- Population
- AI algorithms and their implementation in clinical neurology settings
- Intervention
- AI algorithms applied to clinical neurology practice
Primary outcomes
Rate of AI algorithm translation into actionable clinical practice; Identification of barriers in the AI-to-bedside translational pipeline