Evidence map›Paper›PMID 37398017›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Fostering transparent medical image AI via an image-text foundation model grounded in medical literature.

Chanwoo Kim, Soham U Gadgil, Alex J DeGrave, Zhuo Ran Cai, Roxana Daneshjou, Su-In Lee

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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

5 · Who and what money

Authors and funding

6 authors.

Chanwoo KimPaul G. Allen School of Computer Science and Engineering, University of Washington.
Soham U GadgilPaul G. Allen School of Computer Science and Engineering, University of Washington.
Alex J DeGravePaul G. Allen School of Computer Science and Engineering, University of Washington.
Zhuo Ran CaiProgram for Clinical Research and Technology, Stanford University.
Roxana DaneshjouDepartment of Dermatology, Stanford School of Medicine.
Su-In LeePaul G. Allen School of Computer Science and Engineering, University of Washington.

Funding

University of Washington Medical Scientist Training Program: MD/PhDT32GM007266 · NIGMS · UNIVERSITY OF WASHINGTON · PI HORWITZ, MARSHALL S. · 1985 to 2023
$30.4M
Postgraduate Training Program in Epithelial BiologyT32AR007422 · NIAMS · STANFORD UNIVERSITY · PI PAUL KHAVARI · 1986 to 2026
$5.0M
Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic TargetsR01AG061132 · NIA · UNIVERSITY OF WASHINGTON · PI LEE, SU-IN · 2019 to 2023
$2.9M
Opening the Black Box of Machine Learning ModelsR35GM128638 · NIGMS · UNIVERSITY OF WASHINGTON · PI LEE, SU-IN · 2018 to 2022
$1.9M
NIAMS NIH HHS T32 AR007422NIA NIH HHS R01 AG061132NIGMS NIH HHS R35 GM128638NIGMS NIH HHS T32 GM007266
6 · The paper itself

Abstract

Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET (

Identifiers

PMID37398017
PMCPMC10312868

What OpenQuestion holds

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LicenceCC BY-NC
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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.