Evidence map›Paper›PMID 37863621›Full record

ArticleSeminars in vascular surgery2023

Mature artificial intelligence- and machine learning-enabled medical tools impacting vascular surgical care: A scoping review of late-stage, US Food and Drug Administration-approved or cleared technologies relevant to vascular surgeons.

David P Stonko, Caitlin W Hicks

Abstract readScoping Review
In one paragraph

Article in Seminars in vascular surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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

2 authors.

David P StonkoDivision of Vascular Surgery and Endovascular Therapy, Department of Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Halsted 668, Baltimore, MD 21287.
Caitlin W HicksDivision of Vascular Surgery and Endovascular Therapy, Department of Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Halsted 668, Baltimore, MD 21287. Electronic address: chicks11@jhmi.edu.

Funding

Risk Factors and Outcomes Associated with Late-Life Diabetic Peripheral NeuropathyK23DK124515 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI HICKS, CAITLIN WHITNEY · 2020 to 2024
$898k
NIDDK NIH HHS K23 DK124515
6 · The paper itself

Abstract

Artificial intelligence and machine learning (AI/ML)-enabled tools are shifting from theoretical or research-only applications to mature, clinically useful tools. The goal of this article was to provide a scoping review of the most mature AI/ML-enabled technologies reviewed and cleared by the US Food and Drug Administration relevant to the field of vascular surgery. Despite decades of slow progress, this landscape is now evolving rapidly, with more than 100 AI/ML-powered tools being approved by the US Food and Drug Administration each year. Within the field of vascular surgery specifically, this review identified 17 companies with mature technologies that have at least one US Food and Drug Administration clearance, all occurring between 2016 and 2022. The maturation of these technologies appears to be accelerating, with improving regulatory clarity and clinical uptake. The early AI/ML-powered devices extend or amplify clinically entrenched platform technologies and tend to be focused on the diagnosis or evaluation of time-sensitive, clinically important pathologies (eg, reading Digital Imaging and Communications in Medicine-compliant computed tomography images to identify pulmonary embolism), or when physician efficiency or time savings is improved (eg, preoperative planning and intraoperative guidance). The majority (>75%) of these technologies are at the intersection of radiology and vascular surgery. It is becoming increasingly important that the contemporary vascular surgeon understands this shifting paradigm, as these once-nascent technologies are finally maturing and will be encountered with increasingly regularity in daily clinical practice.

Indexed as

RadiologySurgeonsArtificial IntelligenceHumansMachine LearningUnited StatesUnited States Food and Drug AdministrationAortic surgeryArtificial intelligenceMachine learningMature machine learningVascular surgery

Identifiers

PMID37863621
PMCPMC10589449

What OpenQuestion holds

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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.