View in EDS HTML Full Text PDF Full Text

Hybrid artificial intelligence frameworks for otoscopic diagnosis: Integrating convolutional neural networks and large language models toward real-time mobile health.

Saved in:
Bibliographic Details
Title: Hybrid artificial intelligence frameworks for otoscopic diagnosis: Integrating convolutional neural networks and large language models toward real-time mobile health.
Authors: Chu YC; Department of Information Management, Taipei Veterans General Hospital, Taipei.; Big Data Canter, Taipei Veterans General Hospital, Taipei.; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei., Chen YC; Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Municipal Gangshan Hospital (Outsourced by Show-Chwan Memorial Hospital), Kaohsiung.; Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei., Hsu CY; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei.; Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei., Kuo CT; Department of Information Management, Taipei Veterans General Hospital, Taipei.; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei., Cheng YF; Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei.; Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei.; Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei.; Department of Medical Research, Taipei Veterans General Hospital, Taipei., Lin KH; Department of Information Management, Taipei Veterans General Hospital, Taipei.; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei., Liao WH; Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei.; Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei.
Source: Digital health [Digit Health] 2025 Nov 20; Vol. 11, pp. 20552076251395449. Date of Electronic Publication: 2025 Nov 20 (Print Publication: 2025).
Publication Type: Journal Article
Journal Info: Publisher: SAGE Publications Ltd Country of Publication: United States NLM ID: 101690863 Publication Model: eCollection Cited Medium: Print ISSN: 2055-2076 (Print) Linking ISSN: 20552076 NLM ISO Abbreviation: Digit Health Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
Description
ISSN:2055-2076
DOI:10.1177/20552076251395449