Evidence map›Paper›PMID 42082064›Full record

ArticleJournal of the American College of Radiology : JACR2026

Using Artificial Intelligence to Improve Timeliness of Follow-Up in Breast Cancer Screening.

Diana L Miglioretti, Matt Ponzini, Ojas A Ramwala, Evan de Bie, Shadi Aminololama-Shakeri, Elizabeth Morris, Christoph I Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American College of Radiology : JACR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Diana L MigliorettiDivision Chief of Biostatistics and co-leader of the Population Sciences and Cancer Control Program of the Comprehensive Cancer Center, Department of Public Health Sciences, University of California, Davis, California, and is also from the Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington. Electronic address: dmiglioretti@health.ucdavis.edu.
Matt PonziniDepartment of Public Health Sciences, University of California, Davis, California.
Ojas A RamwalaDepartment of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, and from the Department of Radiology, University of Washington School of Medicine, Seattle, Washington.
Evan de BieDepartment of Public Health Sciences, University of California, Davis, California.
Shadi Aminololama-ShakeriDepartment of Radiology, University of California, Davis, Sacramento, California; Division Chief of Breast Radiology, Program Director of the Breast Imaging Fellowship, and Vice Chair of Academic Affairs; School of Medicine Admissions Committee Chair.
Elizabeth MorrisChair of the Department of Radiology, University of California, Davis, Sacramento, California.
Christoph I LeeVice Chair of Research for the Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin; Deputy Editor of JACR.

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARLA M KERLIKOWSKE · 2011 to 2026
$52.9M
Population-Based Evaluation of Artificial Intelligence for Mammography Priorto Widespread Clinical TranslationR01CA262023 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CHRISTOPH I LEE · 2022 to 2026
$3.2M
NCI NIH HHS P01 CA154292NCI NIH HHS R01 CA262023
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Identifiers

PMID42082064
PMCPMC13265059

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.