ArticleRadiology2025
Influence of AI Decision Support on Radiologists' Performance and Visual Search in Screening Mammography.
Article in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.European radiology · 2026Article
- Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study.European radiology · 2026Article
- A National Audit of Mammography Systems Settings That May Affect the Output of Artificial Intelligence Software.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in medicine: a scoping review of the risk of deskilling and loss of expertise among physicians.ESMO real world data and digital oncology · 2026Review
- Eye tracking as a tool to quantify the effects of CAD display on radiologists' interpretation of chest radiographs.European journal of radiology open · 2026Article
- Commercial artificial intelligence-assisted performance and interpretation time of first-on-call radiology residents using computed tomography pulmonary angiography to detect pulmonary embolism: a multireader, multicenter study.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
Corrections and comments
- Commented on by
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Artificial intelligence (AI) decision support may improve radiologist performance during screening mammography interpretation, but its effect on radiologists' visual search behavior remains unclear. Purpose To compare radiologist performance and visual search patterns when reading screening mammograms with and without an AI decision support system. Materials and Methods In this retrospective multireader multicase study, 12 breast screening radiologists with 4-32 years of experience (median, 12 years) from 10 institutions evaluated screening mammograms acquired between September 2016 and May 2019. Assessments were conducted unaided and with a Food and Drug Administration-approved, European Commission-marked AI decision support system, which assigns a region suspicion score from 1 to 100, with 100 indicating the highest malignancy likelihood. An eye tracker monitored readers' eye movements. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity between unaided and AI-assisted reading were compared using multireader multicase analysis software. Reading times, breast fixation coverage (percentage breast covered by fixations within 2.5° visual angle radius), fixation time, and time to first fixation within the lesion region were compared using bootstrap resampling (
Indexed as
Identifiers
What OpenQuestion holds
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.