ArticleJournal of the American Medical Informatics Association : JAMIA
Investigating the link between radiologists' gaze, diagnostic decision, and image content.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 85 citations in OpenAlex.
- Effect of conspicuity level on dental students' visual attention to impacted maxillary canines on panoramic radiographs: an eye-tracking study.BMC oral health · 2026Article
- Microsaccade Dynamics Enhance Nodule Detection and Discriminate Reader Expertise Levels During Chest Radiography.Research square · 2026Article
- Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.NPJ digital medicine · 2025Review
- Machine Learning-Based Analysis of Optical Coherence Tomography Angiography Images for Age-Related Macular Degeneration.Biomedicines · 2025Article
- Collaborative Integration of AI and Human Expertise to Improve Detection of Chest Radiograph Abnormalities.Radiology. Artificial intelligence · 2025Article
- Modeling radiologists' cognitive processes using a digital gaze twin to enhance radiology training.Scientific reports · 2025Article
- The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review.IEEE journal of biomedical and health informatics · 2024Review
- Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods.IEEE open journal of engineering in medicine and biology · 2024Article
- Visual Image Annotation for Bowel Obstruction: Repeatability and Agreement with Manual Annotation and Neural Networks.Journal of digital imaging · 2023Article
- Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.Scientific data · 2021Article
- Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers.Scientific reports · 2020Article
- Computational framework for fusing eye movements and spoken narratives for image annotation.Journal of vision · 2020Article
- Association of Electronic Health Record Use With Physician Fatigue and Efficiency.JAMA network open · 2020Article
- Factors affecting perception of the normal pediatric appendix on sonography.The ultrasound journal · 2019Article
- Visual search in breast imaging.The British journal of radiology · 2019Article
- Investigating the effect of clinical history before electrocardiogram interpretation on the visual behavior and interpretation accuracy of clinicians.Scientific reports · 2019Article
- Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.Journal of digital imaging · 2019Article
- A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.Medical image analysis · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms.
methodsGaze data and diagnostic decisions were collected from three breast imaging radiologists and three radiology residents who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Image analysis was performed in mammographic regions that attracted radiologists' attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored.
resultsBy pooling the data from all readers, machine learning produced highly accurate predictive models linking image content, gaze, and cognition. Potential linking of those with diagnostic error was also supported to some extent. Merging readers' gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the readers' diagnostic errors while confirming 97.3% of their correct diagnoses. The readers' individual perceptual and cognitive behaviors could be adequately predicted by modeling the behavior of others. However, personalized tuning was in many cases beneficial for capturing more accurately individual behavior.
conclusionsThere is clearly an interaction between radiologists' gaze, diagnostic decision, and image content which can be modeled with machine learning algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.