Visual images in radiography: pareidolia as a useful tool for physicians and artificial intelligence

This article explored the role of pareidolia in radiography and its potential in improving diagnosis and medical personnel training. Pareidolia is the phenomenon of perceiving familiar patterns in random objects, such as faces on the moon’s surface and animal figures in clouds. In radiography, pareidolia can manifest as recognizable patterns in medical images. This enables radiographers to identify abnormalities and improve their diagnostic skills. This work aimed to evaluate pareidolia caused by the interpretation of X-ray images and determine its potential applications. From June to December 2023, a competition was held to create a dataset of pareidolic illusions. Thirty-one individuals participated, including medical imaging specialists who had access to radiographic images. Images from nine additional participants were obtained outside the competition. Overall, 71 images were received. Participants uploaded images using a form on Yandex Forms. Data quality was ensured by clearly defined inclusion and exclusion criteria. Data analysis revealed that people most frequently perceive human faces, animal snouts, and the heart symbol. These findings indicate the possibility of further research. This article discusses the potential applications of pareidolia in developing neural networks for automated medical image analysis and in educational activities that stimulate creative thinking and association. Moreover, the article emphasizes the importance of ongoing research in this area to develop effective diagnostic tools and educational programs by expanding the evidence base.

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Publication Details

Journal
Digital Diagnostics
Published
2025-09-12
DOI
https://doi.org/10.17816/dd641705
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

Visual images in radiography: pareidolia as a useful tool for physicians and artificial intelligence

Olga V. Omelyanskaya, A. V. Solovev, Maria A. Zelenova, Tatiana Bobrovskaya
Digital Diagnostics
Artificial Intelligence in Healthcare and Education
article

Visual images in radiography: pareidolia as a useful tool for physicians and artificial intelligence

Olga V. Omelyanskaya, A. V. Solovev, Maria A. Zelenova, Tatiana Bobrovskaya
article en

Abstract

This article explored the role of pareidolia in radiography and its potential in improving diagnosis and medical personnel training. Pareidolia is the phenomenon of perceiving familiar patterns in random objects, such as faces on the moon’s surface and animal figures in clouds. In radiography, pareidolia can manifest as recognizable patterns in medical images. This enables radiographers to identify abnormalities and improve their diagnostic skills. This work aimed to evaluate pareidolia caused by the interpretation of X-ray images and determine its potential applications. From June to December 2023, a competition was held to create a dataset of pareidolic illusions. Thirty-one individuals participated, including medical imaging specialists who had access to radiographic images. Images from nine additional participants were obtained outside the competition. Overall, 71 images were received. Participants uploaded images using a form on Yandex Forms. Data quality was ensured by clearly defined inclusion and exclusion criteria. Data analysis revealed that people most frequently perceive human faces, animal snouts, and the heart symbol. These findings indicate the possibility of further research. This article discusses the potential applications of pareidolia in developing neural networks for automated medical image analysis and in educational activities that stimulate creative thinking and association. Moreover, the article emphasizes the importance of ongoing research in this area to develop effective diagnostic tools and educational programs by expanding the evidence base.

Digital DiagnosticsVol. 6(3)
Weatherford College (US), City Clinical Hospital (RU)
Openalex Percentile: Top 80%
Artificial Intelligence in Healthcare and Education
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