Evidence map›Paper›PMID 41315669›Full record

ReviewNPJ digital medicine2025

Protecting patient privacy in tabular synthetic health data: a regulatory perspective.

Lisa Pilgram, Haksoo Ko, Adeline Tung, Khaled El Emam

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

4 authors.

Lisa PilgramSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
Haksoo KoSeoul National University, Seoul, South Korea.
Adeline TungPersonal Data Protection Commission, Singapore, Singapore.
Khaled El EmamSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada. kelemam@ehealthinformation.ca.

Funding

CIHR Canada Research Chairs programDeutsche Forschungsgemeinschaft 530282197Natural Sciences and Engineering Research Council of Canada RGPIN-2022-04811
6 · The paper itself

Abstract

Synthetic tabular data generation (SDG) is increasingly important in healthcare research and innovation while preserving patients' privacy. However, ethical concerns remain, primarily over residual privacy vulnerability and insufficient oversight. This review analyzes the only published SDG regulatory guidelines to date, from United Kingdom, Singapore, and South Korea. All emphasize privacy, acknowledging synthetic data is not inherently free from disclosure risks. Thresholds for sufficiently low risk are yet to be determined.

Identifiers

PMID41315669
PMCPMC12663144

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.