Discovery, Learning, and Experimentation With Artificial Intelligence-Based Tools at the Point of Care-Perils and Opportunity.

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

2 citing papers in PubMed.

1. Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier.PLOS digital health · 2024
   Article
2. Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models.European heart journal. Digital health · 2023
   Article

Corrections and comments

• Comment on
  Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.2021

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as