Evidence map›Paper›PMID 42106553›Full record

ArticleNPJ digital medicine2026

Dataset documentation for responsible AI: analysis of suitability and usage for health datasets.

Anna Heinke, Lingling Huang, Kyongmi U Simpkins, Fritz Gerald P Kalaw, Apoorva Karsolia, Kiratjit Singh, Sanjay Soundarajan, Benjamin Panny, Camille Nebeker, Sally L Baxter and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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

14 authors.

Anna Heinke *Jacobs Retina Center, La Jolla, CA, USA.
Lingling Huang *Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, 92037, USA.
Kyongmi U Simpkins *Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, 92037, USA.
Fritz Gerald P KalawViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, 92037, USA.
Apoorva KarsoliaViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, 92037, USA.
Kiratjit SinghDepartment of Electrical and Computer Engineering, University of California Davis, Davis, CA, USA.
Sanjay SoundarajanFAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, USA.
Benjamin PannyIntelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Camille NebekerHerbert Wertheim School of Public Health & Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
Sally L BaxterViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, 92037, USA.
Cecilia S LeeDepartment of Ophthalmology, University of Washington, Seattle, WA, USA.
Aaron Y LeeDepartment of Ophthalmology, University of Washington, Seattle, WA, USA.
Bhavesh PatelFAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, USA. bpatel@calmi2.org.
AI-READI Consortium

Funding

Bridge2AI:Salutogenesis Data Generation ProjectOT2OD032644 · OD · WASHINGTON UNIVERSITY · PI BAXTER, SALLY LIU, CHUTE, CHRISTOPHER G · 2022 to 2025
$32.7M
National Institutes of Health Bridge2AI Project OT2OD032644NIH HHS OT2 OD032644
6 · The paper itself

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare, but also raising concerns about algorithmic biases that mostly stem from the training data. It is widely supported that transparent dataset documentation is key to enabling responsible AI development. Several standardized dataset documentation approaches have been established, such as Datasheet, Dataset Nutrition Label, Accountability Documentation, Healthsheet, and Data Card. However, their suitability and usage for health datasets remain unclear. In this Analysis, we compared all five approaches and evaluated their alignment with the STANDING Together Recommendations for Documentation of Health Datasets. We also investigated their real-world usage and gathered insights from generators and consumers of health datasets. Our findings reveal that none of these documentation approaches are used widely or fully suited for health datasets. We recommend developing a standard documentation approach for health datasets along with clear guidelines and automation tools to support adoption.

Identifiers

PMID42106553
PMCPMC13377121

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Registered trials

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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.