ReviewJournal of vision2020
What do radiologists look for? Advances and limitations of perceptual learning in radiologic search.
Review in Journal of vision, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Attention capture and satisfaction of search in dental radiography: an eye-tracking study on the impact of dominant pathologies on visual search behavior among dental students.Head & face medicine · 2026Article
- Toward uncertainty-aware manual delineation of brain tumours using eye-tracking and image-derived features.Physics and imaging in radiation oncology · 2026Article
- Microsaccade Dynamics Enhance Nodule Detection and Discriminate Reader Expertise Levels During Chest Radiography.Research square · 2026Article
- Characterizing Visual Neurosurgical Expertise in Brain MRI Visualization Using Eye-Tracking and 3D Fractal Dimension Analysis.Journal of eye movement research · 2026Article
- Modeling radiologists' cognitive processes using a digital gaze twin to enhance radiology training.Scientific reports · 2025Article
- Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption.Computational and structural biotechnology journal · 2024Article
- Why did Rubens add a parrot to Titian's The Fall of Man? A pictorial manipulation of joint attention.Journal of vision · 2024Article
- Enhancing visual perceptual learning using transcranial electrical stimulation: Transcranial alternating current stimulation outperforms both transcranial direct current and random noise stimulation.Journal of vision · 2023Article
- Eye tracking: empirical foundations for a minimal reporting guideline.Behavior research methods · 2023Review
- What the Neuroscience and Psychology of Magic Reveal about Misinformation.Publications (Basel, Switzerland) · 2022Article
- Mandating Limits on Workload, Duty, and Speed in Radiology.Radiology · 2022Review
- Negative cues minimize visual search specificity effects.Vision research · 2022Article
- Enhancement of visual perception by combining transcranial electrical stimulation and visual perceptual training.Medical review (2021) · 2022Review
- Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy.Frontiers in oncology · 2022Article
- Visual Illusions in Radiology: Untrue Perceptions in Medical Images and Their Implications for Diagnostic Accuracy.Frontiers in neuroscience · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Identifying such features would allow the development of perceptual learning training methods targeted to the optimization of radiology training and the reduction of medical error. Here we review attempts to bridge current gaps in understanding with a focus on computational saliency models that characterize and predict gaze behavior in radiologists. There have been great strides toward the accurate prediction of relevant medical information within images, thereby facilitating the development of novel computer-aided detection and diagnostic tools. In some cases, computational models have achieved equivalent sensitivity to that of radiologists, suggesting that we may be close to identifying the underlying visual representations that radiologists use. However, because the relevant bottom-up features vary across task context and imaging modalities, it will also be necessary to identify relevant top-down factors before perceptual expertise in radiology can be fully understood. Progress along these dimensions will improve the tools available for educating new generations of radiologists, and aid in the detection of medically relevant information, ultimately improving patient health.
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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.