Federated learning: a step in the right direction to improve data equity.

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.

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.

Who cites it

4 citing papers in PubMed.

1. Cognitive sovereignty and decolonial public health: reclaiming epistemic authority in the global AI era.Frontiers in public health · 2026
   Article
2. Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026
   Review
3. How AI can help in error detection and prevention in the ICU?Intensive care medicine · 2025
   Article
4. Trash in/trash out? Using routinely collected clinical data for data science in the ICU: Con.Intensive care medicine · 2025
   Article

Corrections and comments

• Commented on by
  Why federated learning will do little to overcome the deeply embedded biases in clinical medicine.2024

Authors and funding

4 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.