Evidence map›Paper›PMID 41960173›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2026

Modeling Workflow, Operational, and Financial Implications of AI-Enabled Same-Day Diagnostic Follow-Up for Screening Mammograms.

Yannan Lin, Anne C Hoyt, Nina M Capiro, Olivia Linden, Luoting Zhuang, Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, Ya-Chen Tina Shih, William Hsu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 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

9 authors.

Yannan LinMedical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Anne C HoytDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Nina M CapiroDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Olivia LindenDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Luoting ZhuangMedical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Mehmet Ulvi Saygi AyvaciNaveen Jindal School of Management, The University of Texas at Dallas, Richardson, Texas.
Mehmet Eren AhsenDepartment of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois.
Ya-Chen Tina ShihDepartment of Radiation Oncology, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, USA.
William HsuMedical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.

Funding

NRSA Training CoreTL1TR001883 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI, Nina Thawata Harawa · 2016 to 2026
$8.6M
AHRQ HHS R33 HS029257NCATS NIH HHS TL1 TR001883
6 · The paper itself

Abstract

While much of the evaluation of artificial intelligence (AI) in healthcare has focused on technical performance metrics such as accuracy or area under the curve, real-world adoption critically depends on how AI reshapes clinical workflows, operations, and revenue streams. Simulation models provide a means to anticipate these impacts before implementation, allowing stakeholders to weigh benefits against potential harm. In this study, we used discrete-event simulation to evaluate an AI-assisted workflow for same-day diagnostic breast imaging following abnormal screening mammograms. The revised workflow captured an additional of 1.1% mammography screening patients who might otherwise be lost to follow-up. It also eliminated the need for a second visit for diagnostic workup for 11% of mammography screening patients. It also increased daily work relative value units by 4.8%, translating to an estimated $15,979 in additional annual gain, while extending clinic operating hours by 2.9%, amounting to 109.5 hours annually. These findings highlight how simulation modeling can inform the operational and financial implications of AI adoption in imaging workflows in clinical practice.

Indexed as

artificial intelligencebreast cancer screeningcost and benefitmammographysame-day servicesimulation

Identifiers

PMID41960173
PMCPMC13061406

What OpenQuestion holds

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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.