Hybrid artificial intelligence frameworks for otoscopic diagnosis: Integrating convolutional neural networks and large language models toward real-time mobile health
This hybrid AI framework substantially improves diagnostic precision and responsiveness in otoscopic evaluation. Its mobile-friendly design supports scalable deployment in telemedicine and primary care, offering a practical solution to enhance ear disease diagnosis in underserved regions.
Authors
- Yuan-Chia Chu (ORCID: https://orcid.org/0000-0002-0997-5168)
- Yen‐Fu Cheng (ORCID: https://orcid.org/0000-0003-1995-5854)
- Yen‐Chi Chen
- Wen‐Huei Liao (ORCID: https://orcid.org/0000-0001-7852-4654)
- Chien‐Yeh Hsu
- Kuan‐Hsun Lin (ORCID: https://orcid.org/0009-0002-8058-073X)
- Chen‐Tsung Kuo
Institutions
- National Yang Ming Chiao Tung University (TW)
- National Taipei University of Nursing and Health Science (TW)
- Taipei Veterans General Hospital (TW)
- Chang Bing Show Chwan Memorial Hospital (TW)
- Taipei Medical University (TW)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251395449
- Citations
- 1
- Primary Topic
- Ear Surgery and Otitis Media
- Type
- article
- Field-Weighted Citation Impact
- 1.85