Evidence map›Paper›PMID 37577564›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Human and computer attention in assessing genetic conditions.

Dat Duong, Anna Rose Johny, Suzanna Ledgister Hanchard, Chris Fortney, Fabio Hellmann, Ping Hu, Behnam Javanmardi, Shahida Moosa, Tanviben Patel, Susan Persky and 7 more

Open access · greenAbstract 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, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 4 institutions in 3 countries.

Dat DuongMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
Anna Rose JohnyInstitute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Suzanna Ledgister HanchardMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
Chris FortneySocial and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of America.
Fabio HellmannChair for Human-Centered Artificial Intelligence, University of Augsburg, Germany.
Ping HuMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
Behnam JavanmardiInstitute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Shahida MoosaDivision of Molecular Biology and Human Genetics, Stellenbosch University, Stellenbosch, South Africa.
Tanviben PatelMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
Susan PerskySocial and Behavioral Research Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of America.
Ömer SümerChair for Human-Centered Artificial Intelligence, University of Augsburg, Germany.
Cedrik Tekendo-NgongangMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
Tzung-Chien HsiehInstitute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Rebekah L WaikelMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
Elisabeth AndréChair for Human-Centered Artificial Intelligence, University of Augsburg, Germany.
Peter KrawitzInstitute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Benjamin D SolomonMedical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of Americav.
National Human Genome Research Institute · USUniversity of Bonn · DEUniversity of Augsburg · DEStellenbosch University · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning (DL) and other types of artificial intelligence (AI) are increasingly used in many biomedical areas, including genetics. One frequent use in medical genetics involves evaluating images of people with potential genetic conditions to help with diagnosis. A central question involves better understanding how AI classifiers assess images compared to humans. To explore this, we performed eye-tracking analyses of geneticist clinicians and non-clinicians. We compared results to DL-based saliency maps. We found that human visual attention when assessing images differs greatly from the parts of images weighted by the DL model. Further, individuals tend to have a specific pattern of image inspection, and clinicians demonstrate different visual attention patterns than non-clinicians.

Identifiers

PMID37577564
PMCPMC10418573
OpenAlexW4385335482

What OpenQuestion holds

Textmetadata
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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.