Evidence map›Paper›PMID 40417477›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing Following an Abnormal Screening Mammogram.

Yannan Lin, Anne C Hoyt, Vladimir G Manuel, Moira Inkelas, Cleo K Maehara, Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, William Hsu

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

8 authors.

Yannan LinMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Anne C HoytDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Vladimir G ManuelDepartment of Family Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Moira InkelasUCLA Clinical and Translational Science Institute, Los Angeles, CA, USA.
Cleo K MaeharaMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Mehmet Ulvi Saygi AyvaciNaveen Jindal School of Management at the University of Texas at Dallas, Richardson, TX, USA.
Mehmet Eren AhsenDepartment of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
William HsuMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.

Funding

AHRQ HHS R21 HS029257AHRQ HHS R33 HS029257
6 · The paper itself

Abstract

Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aided same-day diagnostic imaging workup to reduce recall rates following abnormal screening mammograms and alleviate patient anxiety while waiting for the diagnostic examinations. Using discrete simulation, we found minimal disruption to the workflow (a 4% reduction in daily patient volume or a 2% increase in operating time) under specific conditions: operation from 9 am to 12 pm with all radiologists managing all patient types (screenings, diagnostics, and biopsies). Costs specific to the AI-aided same-day diagnostic workup include AI software expenses and potential losses from unused pre-reserved slots for same-day diagnostic workups. These simulation findings can inform the implementation of an AI-aided same-day diagnostic workup, with future research focusing on its potential benefits, including improved patient satisfaction, reduced anxiety, lower recall rates, and shorter time to cancer diagnoses and treatment.

Indexed as

Artificial IntelligenceBreast NeoplasmsMammographyWorkflowComputer SimulationFemaleHumans

Identifiers

PMID40417477
PMCPMC12099379

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.