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Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review
  1. Therese L Canares1,
  2. Weiyao Wang2,
  3. Mathias Unberath2,
  4. James H Clark3
  1. 1 Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  2. 2 Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland, USA
  3. 3 Otolaryngology-HNS, Johns Hopkins Medicine School of Medicine, Baltimore, Maryland, USA
  1. Correspondence to Dr James H Clark, Otolaryngology-HNS, Johns Hopkins Medicine School of Medicine, Baltimore, MD 21224, USA; jclark79{at}


AI relates broadly to the science of developing computer systems to imitate human intelligence, thus allowing for the automation of tasks that would otherwise necessitate human cognition. Such technology has increasingly demonstrated capacity to outperform humans for functions relating to image recognition. Given the current lack of cost-effective confirmatory testing, accurate diagnosis and subsequent management depend on visual detection of characteristic findings during otoscope examination. The aim of this manuscript is to perform a comprehensive literature review and evaluate the potential application of artificial intelligence for the diagnosis of ear disease from otoscopic image analysis.

  • ear
  • external
  • diagnostic tests
  • routine

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  • Contributors All listed authors were involved in designing and writing of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; externally peer reviewed.