Evidence map›Paper›PMID 31735833›Full record

ReviewVision (Basel, Switzerland)2019

Eye Movements in Medical Image Perception: A Selective Review of Past, Present and Future.

Chia-Chien Wu, Jeremy M Wolfe

Abstract readReview
In one paragraph

Review in Vision (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Digital pathology: the effect of experience on visual search behavior.Journal of medical imaging (Bellingham, Wash.) · 2022
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Visual Search: How Do We Find What We Are Looking For?Annual review of vision science · 2020
    Review
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Chia-Chien WuVisual Attention Lab, Department of Surgery, Brigham & Women's Hospital, 65 Landsdowne St, Cambridge, MA 02139, USA.ORCID 0000-0001-9662-3736
Jeremy M WolfeVisual Attention Lab, Department of Surgery, Brigham & Women's Hospital, 65 Landsdowne St, Cambridge, MA 02139, USA.ORCID 0000-0002-6475-1984

Funding

Prevalence effects in visual search: Theoretical and practical implicationsR01EY017001 · NEI · BRIGHAM AND WOMEN'S HOSPITAL · PI Jeremy M Wolfe · 2007 to 2026
$8.2M
Improving Perception in Digital Breast TomographyR01CA207490 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI WOLFE, JEREMY M · 2016 to 2025
$4.6M
NCI NIH HHS R01 CA207490NEI NIH HHS R01 EY017001NIH HHS EY017001; CA207490
6 · The paper itself

Abstract

The eye movements of experts, reading medical images, have been studied for many years. Unlike topics such as face perception, medical image perception research needs to cope with substantial, qualitative changes in the stimuli under study due to dramatic advances in medical imaging technology. For example, little is known about how radiologists search through 3D volumes of image data because they simply did not exist when earlier eye tracking studies were performed. Moreover, improvements in the affordability and portability of modern eye trackers make other, new studies practical. Here, we review some uses of eye movements in the study of medical image perception with an emphasis on newer work. We ask how basic research on scene perception relates to studies of medical 'scenes' and we discuss how tracking experts' eyes may provide useful insights for medical education and screening efficiency.

Indexed as

eye movementsmedical image perceptionvisual search

Identifiers

PMID31735833
PMCPMC6802791

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Registered trials

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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.