Idiosyncratic biases in the perception of medical images

Zixuan Wang* (Corresponding Author), Mauro Manassi, Zhihang Ren, Cristina Ghirardo, Teresa Canas-Bajo, Yuki Murai, Min Zhou, David Whitney

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Introduction: Radiologists routinely make life-altering decisions. Optimizing these decisions has been an important goal for many years and has prompted a great deal of research on the basic perceptual mechanisms that underlie radiologists’ decisions. Previous studies have found that there are substantial individual differences in radiologists’ diagnostic performance (e.g., sensitivity) due to experience, training, or search strategies. In addition to variations in sensitivity, however, another possibility is that radiologists might have perceptual biases—systematic misperceptions of visual stimuli. Although a great deal of research has investigated radiologist sensitivity, very little has explored the presence of perceptual biases or the individual differences in these.

Methods: Here, we test whether radiologists’ have perceptual biases using controlled artificial and Generative Adversarial Networks-generated realistic medical images. In Experiment 1, observers adjusted the appearance of simulated tumors to match the previously shown targets. In Experiment 2, observers were shown with a mix of real and GAN-generated CT lesion images and they rated the realness of each image.

Results: We show that every tested individual radiologist was characterized by unique and systematic perceptual biases; these perceptual biases cannot be simply explained by attentional differences, and they can be observed in different imaging modalities and task settings, suggesting that idiosyncratic biases in medical image perception may widely exist.

Discussion: Characterizing and understanding these biases could be important for many practical settings such as training, pairing readers, and career selection for radiologists. These results may have consequential implications for many other fields as well, where individual observers are the linchpins for life-altering perceptual decisions.
Original languageEnglish
Article number1049831
Number of pages15
JournalFrontiers in Psychology
Early online date19 Dec 2022
Publication statusPublished - 19 Dec 2022

Bibliographical note

This work was supported in part by the National Institutes of Health (grant number: R01 CA236793-01). Publication made possible in part by support from the Berkeley Research Impact Initiative (BRII) sponsored by the UC Berkeley Library.

Raw data from Experiment 1 and 2 were obtained from Manassi et al. (2021) and Ren et al. (2022). Parts of these datasets have been previously presented at conferences including VSS and ECVP.


  • medical image perception
  • Individual differences
  • GAN-simulated medical images
  • perceptual biases
  • radiologist performance


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