{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:46:43Z","timestamp":1788281203181,"version":"build-2803163510"},"reference-count":29,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100020950","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC113-2320-B-075-010"],"award-info":[{"award-number":["NSTC113-2320-B-075-010"]}],"id":[{"id":"10.13039\/501100020950","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020950","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC114-2320-B075-009"],"award-info":[{"award-number":["NSTC114-2320-B075-009"]}],"id":[{"id":"10.13039\/501100020950","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011912","name":"Taipei Veterans General Hospital","doi-asserted-by":"publisher","award":["V114C-149"],"award-info":[{"award-number":["V114C-149"]}],"id":[{"id":"10.13039\/501100011912","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011912","name":"Taipei Veterans General Hospital","doi-asserted-by":"publisher","award":["V114E-006-2"],"award-info":[{"award-number":["V114E-006-2"]}],"id":[{"id":"10.13039\/501100011912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Otitis media remains a significant global health concern, particularly in resource-limited settings where timely diagnosis is challenging. Artificial intelligence (AI) offers promising solutions to enhance diagnostic accuracy in mobile health applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study introduces a hybrid AI framework that integrates convolutional neural networks (CNNs) for image classification with large language models (LLMs) for clinical reasoning, enabling real-time otoscopic diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a dual-path system combining CNN-based feature extraction with LLM-supported interpretation. The framework was optimized for mobile deployment, with lightweight models operating on-device and advanced reasoning performed via secure cloud APIs. A dataset of 10,465 otoendoscopic images (expanded from 2820 original clinical images through data augmentation) across 10 middle-ear conditions was used for training and validation. Diagnostic performance was benchmarked against clinicians of varying expertise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The hybrid CNN\u2013LLM system achieved an overall diagnostic accuracy of 97.6%, demonstrating the synergistic benefit of combining CNN-driven visual analysis with LLM-based clinical reasoning. The system delivered sub-200\u2005ms feedback and achieved specialist-level performance in identifying common ear pathologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251395449","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T17:25:34Z","timestamp":1763659534000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid artificial intelligence frameworks for otoscopic diagnosis: Integrating convolutional neural networks and large language models toward real-time mobile health"],"prefix":"10.1177","volume":"11","author":[{"given":"Yuan-Chia","family":"Chu","sequence":"first","affiliation":[{"name":"Taipei Veterans General Hospital"},{"name":"Big Data Canter, Taipei Veterans General Hospital, Taipei"},{"name":"Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen-Chi","family":"Chen","sequence":"additional","affiliation":[{"name":"Kaohsiung Municipal Gangshan Hospital (Outsourced by Show-Chwan Memorial Hospital)"},{"name":"Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chien-Yeh","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei"},{"name":"Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Tsung","family":"Kuo","sequence":"additional","affiliation":[{"name":"Taipei Veterans General Hospital"},{"name":"Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen-Fu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei"},{"name":"Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei"},{"name":"Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei"},{"name":"Department of Medical Research, Taipei Veterans General Hospital, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8058-073X","authenticated-orcid":false,"given":"Kuan-Hsun","family":"Lin","sequence":"additional","affiliation":[{"name":"Taipei Veterans General Hospital"},{"name":"Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Huei","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, Taipei"},{"name":"Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0036226"},{"key":"e_1_3_3_3_2","article-title":"Economic burden of otitis media globally and an overview of the current scenario to alleviate the disease burden: a systematic review","volume":"28","author":"Bhatia R","year":"2024","unstructured":"Bhatia R, Chauhan A, Rana M, et al. Economic burden of otitis media globally and an overview of the current scenario to alleviate the disease burden: a systematic review. Int Arch Otorhinolaryngol 2024; 28: e552\u2013e558.","journal-title":"Int Arch Otorhinolaryngol"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.63"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1099\/jmm.0.000810"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","unstructured":"Xu Y Habib A-R Crossland G et al. Towards reliable use of artificial intelligence to classify otitis media using otoscopic images: addressing bias and improving data quality. 2025. arXiv preprint arXiv:250718842. 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On large visual language models for medical imaging analysis: an empirical study. In: 2024 IEEE\/ACM conference on connected health: applications, systems and engineering technologies (CHASE), Wilmington, DE, USA, 2024, pp.172\u2013176. IEEE."},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","unstructured":"Ahmed S Sakib SK Das AB. Can large language models challenge CNNs in medical image analysis? 2025. arXiv preprint arXiv:250523503. DOI: 10.48550\/arXiv.2505.23503.","DOI":"10.48550\/arXiv.2505.23503"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15091072"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0244772"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000003210"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","unstructured":"Chu YC Lin KH. Enhancing clinical accuracy in middle ear disease diagnosis with a cloud-based AI system integrating CNNs and LLMs. In: International conference on biomedical and health informatics 2024. ICBHI 2024 (eds KP Lin R Magjarevi\u0107 P de Carvalho) IFMBE Proceedings vol 118 Tainan Taiwan 2024. Cham: Springer 2025. DOI: 10.1007\/978-3-031-86323-3_22.","DOI":"10.1007\/978-3-031-86323-3_22"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1067\/j.cpradiol.2020.12.007"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","unstructured":"Z\u00f6ller N Berger J Lin I et al. Human\u2013AI collectives produce the most accurate differential diagnoses. 2024. arXiv preprint arXiv:240614981. 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