Using Time as a Measure of Impact for AI Systems: Implications in Breast Screening.

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

5 citing papers in PubMed.

1. Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024
   Review
2. Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT).AI in precision oncology · 2024
   Review
3. Updates in Artificial Intelligence for Breast Imaging.Seminars in roentgenology · 2022
   Review
4. Artificial Intelligence: A Primer for Breast Imaging Radiologists.Journal of breast imaging · 2020
   Review
5. Artificial Intelligence for Breast Ultrasound: Will It Impact Radiologists' Accuracy?Journal of breast imaging
   Article

Corrections and comments

• Comment on
  Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.2019

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.