Evidence map›Paper›PMID 42674218›Full record

ArticleJournal of vascular surgery. Venous and lymphatic disorders2026

Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.

Oscar Moreno, Ruoliu Zhang, Andrew Huang, Yanqing Zhao, Amber Clay, Jorge H Ulloa, Brajesh K Lal, Thomas Wakefield, Minhaj S Khaja, Andrea T Obi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of vascular surgery. Venous and lymphatic disorders, 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

11 authors.

Oscar MorenoSection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI. Electronic address: oscarmo@med.umich.edu.
Ruoliu ZhangDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI.
Andrew HuangSection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI.
Yanqing ZhaoSection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI; Division of Vascular and Interventional Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI.
Amber ClaySection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI.
Jorge H UlloaUniversidad de los Andes, Bogotá D.C., Colombia.
Brajesh K LalDepartment of Vascular Surgery, University of Maryland, Baltimore, MD.
Thomas WakefieldSection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI.
Minhaj S KhajaDivision of Vascular and Interventional Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI.
Andrea T ObiSection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI.
Peter HenkeSection of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has the potential to support personalized, multidisciplinary, data-driven care for venous thromboembolism prevention, detection, imaging, management, and follow-up. We reviewed 23 studies; 20 prediction or detection studies were assessed with PROBAST+AI, while 3 evaluated clinical-impact interventions. Of these, 18 of 20 studies (90.0%) were at high risk of bias. Only 7 studies (35%) showed meaningful external or prospective validation, although 5of 7 remained at high risk of bias. Calibration was not reported in 14 studies (70.0%). Discrimination scores ranged from modest to high, but performance varied across cohorts and was often limited by the retrospective design, internal validation, class imbalance, low event counts, and overfitting. Currently, agentic AI remains conceptual and needs human oversight. Future research should emphasize prospective multicenter evaluation, calibration, transparent reporting, regulatory adherence, and standardized bias auditing before AI is used routinely in clinical practice.

Indexed as

Agentic AIArtificial intelligenceExternal validationMachine learningPROBAST+AIRisk of biasVenous thromboembolism

Identifiers

PMID42674218
PMCPMC13634517

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.