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

Abstract

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.

Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy

“…Significant performance differences were found when non-otolaryngologists were asked to differentiate between acute otitis media (AOM), otitis media with effusion (OME), or retracted TMs 8 . These findings are consistent with recent studies that demonstrate that diagnostic accuracy is reduced when non-experts must differentiate between multiple ear disease sub-types 6 , 11 . Otolaryngologists significantly outperformed non-otolaryngologists to identify ear disease sub-types and important pathological characteristics (e.g.…”

“…Otoscopy is performed routinely by multiple healthcare workers including medical students, nurses, general practitioners, emergency medicine physicians, paediatricians, audiologists, and otolaryngologists. However, the ability to accurately interpret otoscopic findings varies by user experience 6 – 10 . Pichichero et al (2005) demonstrated differences between general practitioners, paediatricians, and otolaryngologists to recognise tympanic membrane (TM) abnormalities 8 .…”

“…Otolaryngologists significantly outperformed non-otolaryngologists to identify ear disease sub-types and important pathological characteristics (e.g. tympanic membrane perforations, attic retraction, or myringitis) 6 , 8 .…”

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

“…Recent studies have applied AI to otological imaging in various clinical contexts (Supplemental File 3, available online). These studies combined AI with otoscopy, [22][23][24][25][26]31,32,38,48,52,57,68,[74][75][76]79,81,84,85,93,95 computed tomography (CT), 30,36,41,42,50,62,63,70,71,88 and magnetic resonance imaging (MRI). 43,73,78 Most studies have focused on the image-based otoscopic diagnosis and automated segmentation of temporal bone CT for classifying normal and abnormal mastoid air cells.…”

Human–machine cooperation meta-model for clinical diagnosis by adaptation to human expert’s diagnostic characteristics

“…The differences between training AI models and human experts and their distinct strengths and limitations in the diagnostic process lead to AI-HI cooperation. For instance, AI models have shown statistical bias towards prevalent diseases 13 , 14 , yet maintain remarkable consistency 15 . Conversely, human experts, while not as biased towards prevalent conditions, exhibit significant inter-rater variability 16 , 17 .…”