Evidence map›Paper›PMID 42317812›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026

Vision Foundry: A System for Training Foundational Vision AI Models.

Mahmut S Gokmen, Mitchell A Klusty, Evan W Damron, W Vaiden Logan, Aaron D Mullen, Caroline N Leach, Emily B Collier, Samuel E Armstrong, V K Cody Bumgardner

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 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.

Mahmut S GokmenCenter for Applied AI, University of Kentucky, Lexington, KY.
Mitchell A KlustyCenter for Applied AI, University of Kentucky, Lexington, KY.
Evan W DamronCenter for Applied AI, University of Kentucky, Lexington, KY.
W Vaiden LoganCenter for Applied AI, University of Kentucky, Lexington, KY.
Aaron D MullenCenter for Applied AI, University of Kentucky, Lexington, KY.
Caroline N LeachCenter for Applied AI, University of Kentucky, Lexington, KY.
Emily B CollierCenter for Applied AI, University of Kentucky, Lexington, KY.
Samuel E ArmstrongCenter for Applied AI, University of Kentucky, Lexington, KY.
V K Cody BumgardnerCenter for Applied AI, University of Kentucky, Lexington, KY.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Self-supervised learning (SSL) leverages vast unannotated medical datasets, yet steep technical barriers limit adoption by clinical researchers. We introduce

Identifiers

PMID42317812
PMCPMC13274312

What Socratic 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 Socratic graph.