Evidence map›Paper›PMID 41542222›Full record

ReviewEClinicalMedicine2026

Navigating open data sharing and privacy in the age of clinical AI research: from reidentification to pseudo-reidentification.

Shahin Hallaj, Anna Heinke, Fritz Gerald P Kalaw, Nayoon Gim, Marian Blazes, Julia Owen, Eamon Dysinger, Erik S Benton, Benjamin A Cordier, Nicholas G Evans and 10 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in EClinicalMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

20 authors.

Shahin HallajDivision of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Anna HeinkeDivision of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Fritz Gerald P KalawDivision of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Nayoon GimDepartment of Ophthalmology, University of Washington, Seattle, WA, USA.
Marian BlazesDepartment of Ophthalmology, University of Washington, Seattle, WA, USA.
Julia OwenDepartment of Ophthalmology, University of Washington, Seattle, WA, USA.
Eamon DysingerOregon Health & Science University, Portland, OR, USA.
Erik S BentonOregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA.
Benjamin A CordierKnight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
Nicholas G EvansDepartment of Political Science, University of Massachusetts Lowell, Lowell, MA, USA.
Jennifer Li-Pook-ThanGenetics Department, Stanford University, Stanford, CA, USA.
Michael P SnyderGenetics Department, Stanford University, Stanford, CA, USA.
Camille NebekerHerbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA.
Linda M ZangwillDivision of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Sally L BaxterDivision of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Shannon McWeeneyKnight Cancer Institute, Oregon Health & Science University, Portland, OR, 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.
AI-READI Consortium

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2025 to 2025
$453k
NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Sharing clinical research data is key for increasing the pace of medical discoveries that improve human health. However, concern about study participants' privacy, confidentiality, and safety is a major factor that deters researchers from openly sharing clinical data even after deidentification. This concern is further enhanced by the evolution of artificial intelligence (AI) approaches that pose an ever-increasing threat to the reidentification of study participants. Here, we discuss the challenges AI approaches create that are blurring the lines between identifiable, and non-identifiable data. We present a concept of pseudo-reidentification, and discuss how these challenges provide opportunities for rethinking open data sharing practices in clinical research. We highlight the novel open data sharing approach we have established as part of the AI-READI (Artificial Intelligence Ready, and Exploratory Atlas for Diabetes Insights) project, one of the four Data Generation Projects funded by the National Institutes of Health Common Fund's Bridge2AI Program.

Indexed as

Artificial intelligenceClinical studyData accessData reuseData sharingMachine learning

Identifiers

PMID41542222
PMCPMC12803850

What Socratic holds

Textmetadata
LicenceCC BY
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.