FAIR: a recipe for ensuring fairness in healthcare artificial intelligence.

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

5 citing papers in PubMed.

1. Reliability of foundation models for image retrieval in histopathology.Npj imaging · 2026
   Article
2. Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026
   Review
3. The critical need for an open medical imaging database in Japan: implications for global health and AI development.Japanese journal of radiology · 2025
   Article
4. Quantitative CT Assessment of Skeletal Muscle and Abdominal Fat Distribution: Predictive Value for Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025
   Article
5. Data set terminology of deep learning in medicine: a historical review and recommendation.Japanese journal of radiology · 2024
   Review

Corrections and comments

• Comment on
  Fairness of artificial intelligence in healthcare: review and recommendations.2024

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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