Evidence map›Paper›PMID 39901158›Full record

ArticleJournal of biomedical science2025

The Data Artifacts Glossary: a community-based repository for bias on health datasets.

Rodrigo R Gameiro, Naira Link Woite, Christopher M Sauer, Sicheng Hao, Chrystinne Oliveira Fernandes, Anna E Premo, Alice Rangel Teixeira, Isabelle Resli, An-Kwok Ian Wong, Leo Anthony Celi

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Article in Journal of biomedical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Rodrigo R Gameiro *Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Naira Link Woite *Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Christopher M Sauer *Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sicheng HaoDivision of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA.
Chrystinne Oliveira FernandesLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Anna E PremoLearning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA.
Alice Rangel TeixeiraDepartment of Philosophy, Universitat Autónoma de Barcelona, Barcelona, Spain.
Isabelle ResliSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.
An-Kwok Ian WongDivision of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA.
Leo Anthony CeliLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA. lceli@mit.edu.ORCID http://orcid.org/0000-0001-6712-6626

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fairness of AI are critically dependent on the data it learns from. Biased datasets can lead to AI outputs that perpetuate disparities, particularly affecting social minorities and marginalized groups.

objectiveThis paper introduces the "Data Artifacts Glossary", a dynamic, open-source framework designed to systematically document and update potential biases in healthcare datasets. The aim is to provide a comprehensive tool that enhances the transparency and accuracy of AI applications in healthcare and contributes to understanding and addressing health inequities.

methodsUtilizing a methodology inspired by the Delphi method, a diverse team of experts conducted iterative rounds of discussions and literature reviews. The team synthesized insights to develop a comprehensive list of bias categories and designed the glossary's structure. The Data Artifacts Glossary was piloted using the MIMIC-IV dataset to validate its utility and structure.

resultsThe Data Artifacts Glossary adopts a collaborative approach modeled on successful open-source projects like Linux and Python. Hosted on GitHub, it utilizes robust version control and collaborative features, allowing stakeholders from diverse backgrounds to contribute. Through a rigorous peer review process managed by community members, the glossary ensures the continual refinement and accuracy of its contents. The implementation of the Data Artifacts Glossary with the MIMIC-IV dataset illustrates its utility. It categorizes biases, and facilitates their identification and understanding.

conclusionThe Data Artifacts Glossary serves as a vital resource for enhancing the integrity of AI applications in healthcare by providing a mechanism to recognize and mitigate dataset biases before they impact AI outputs. It not only aids in avoiding bias in model development but also contributes to understanding and addressing the root causes of health disparities.

Indexed as

Artificial IntelligenceDatasets as TopicBiasHumansArtificial intelligenceBiasData Artifacts GlossaryDatasetHealth equityMachine learning

Identifiers

PMID39901158
PMCPMC11792693

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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.