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Peer-reviewedCC BY 4.0Journal of Clinical Medicine

Multimodal Artificial Intelligence for the Clinical Assessment of Otitis Media

  • Jae Ho Chung1,2
  • Yoonseob Lim2,3
  • Hee Won Seo1
  • Sang-Yoon Han1
  1. 1Department of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Republic of Korea
  2. 2Department of HY-KIST Bio-Convergence, College of Medicine, Hanyang University, Seoul 04763, Republic of Korea
  3. 3Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
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Abstract

Acute otitis media (AOM) and otitis media with effusion (OME) can often be recognized from characteristic otoscopic findings. However, patients also present with subtle, overlapping, or resolving findings that are difficult to assign to a single diagnosis from an image alone. If these encounters are excluded because the reference diagnosis is uncertain, artificial intelligence (AI) models may perform well on selected images while providing less help in clinically uncertain cases. This focused narrative review examines whether symptoms, tympanometry, and hearing information can complement otoscopic images and improve the representation of clinically uncertain middle ear disease. Selected studies have combined images with acoustic measurements, pure-tone audiometry, or patient information. Their findings support feasibility, but differences in diagnostic targets and study design limit conclusions about benefit in uncertain AOM and OME. We propose that future systems account for diagnostically challenging cases during development and include them in clinical evaluation, while describing the current ear status alongside any diagnostic label. Large language models may help express supported findings, their clinical interpretation, and remaining uncertainty in readable language. Such explanations require evaluation independent of classification accuracy. The clinical value of multimodal AI should be established by its ability to support appropriate decisions in representative patients, including those with incomplete or discordant findings.

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Cite

BibTeXRIS
BibTeX
@article{chung2026multimodal,
  title = {{Multimodal Artificial Intelligence for the Clinical Assessment of Otitis Media}},
  author = {Jae Ho Chung and Yoonseob Lim and Hee Won Seo and Sang-Yoon Han},
  journal = {Journal of Clinical Medicine},
  year = {2026},
  month = oct,
  doi = {10.3390/jcm15218405},
  url = {https://www.mdpi.com/2077-0383/15/21/8405},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multimodal Artificial Intelligence for the Clinical Assessment of Otitis Media
AU  - Jae Ho Chung
AU  - Yoonseob Lim
AU  - Hee Won Seo
AU  - Sang-Yoon Han
PY  - 2026
DA  - 2026/10/28
JO  - Journal of Clinical Medicine
DO  - 10.3390/jcm15218405
UR  - https://www.mdpi.com/2077-0383/15/21/8405
AB  - Acute otitis media (AOM) and otitis media with effusion (OME) can often be recognized from characteristic otoscopic findings. However, patients also present with subtle, overlapping, or resolving findings that are difficult to assign to a single diagnosis from an image alone. If these encounters are excluded because the reference diagnosis is uncertain, artificial intelligence (AI) models may perform well on selected images while providing less help in clinically uncertain cases. This focused narrative review examines whether symptoms, tympanometry, and hearing information can complement otoscopic images and improve the representation of clinically uncertain middle ear disease. Selected studies have combined images with acoustic measurements, pure-tone audiometry, or patient information. Their findings support feasibility, but differences in diagnostic targets and study design limit conclusions about benefit in uncertain AOM and OME. We propose that future systems account for diagnostically challenging cases during development and include them in clinical evaluation, while describing the current ear status alongside any diagnostic label. Large language models may help express supported findings, their clinical interpretation, and remaining uncertainty in readable language. Such explanations require evaluation independent of classification accuracy. The clinical value of multimodal AI should be established by its ability to support appropriate decisions in representative patients, including those with incomplete or discordant findings.
SN  - 2077-0383
ER  -