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Differential Biases and Variabilities of Deep Learning-Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study

 Dongchul Cha  ;  Chongwon Pae  ;  Se A Lee  ;  Gina Na  ;  Young Kyun Hur  ;  Ho Young Lee  ;  A Ra Cho  ;  Young Joon Cho  ;  Sang Gil Han  ;  Sung Huhn Kim  ;  Jae Young Choi  ;  Hae-Jeong Park 
 JMIR MEDICAL INFORMATICS, Vol.9(12) : e33049, 2021-12 
Journal Title
Issue Date
artificial intelligence ; computer-aided diagnosis ; convolutional neural network ; deep learning, class imbalance problem ; eardrum ; human-machine cooperation ; otology ; otoscopy
Background: Deep learning (DL)-based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application.

Objective: This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems.

Methods: We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models.

Results: Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07).

Conclusions: Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Emergency Medicine (응급의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Otorhinolaryngology (이비인후과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sung Huhn(김성헌)
Na, Gina(나지나)
Park, Hae Jeong(박해정) ORCID logo https://orcid.org/0000-0002-4633-0756
Cho, Young Joon(조영준)
Cha, Dongchul(차동철) ORCID logo https://orcid.org/0000-0002-0043-5026
Choi, Jae Young(최재영) ORCID logo https://orcid.org/0000-0001-9493-3458
Han, Sang Gil(한상길)
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