ReviewTrauma surgery & acute care open2026
Evaluation framework for assessing the quality and safety of artificial intelligence solutions for the acute care surgeon: a narrative review from the Eastern Association for the Surgery of Trauma quality, safety, and outcomes committee.
Review in Trauma surgery & acute care open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Beyond performance metrics: evaluating clinical artificial intelligence (AI) post deployment.Trauma surgery & acute care open · 2026Article
Corrections and comments
- Commented on by
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has been developing as a field for decades. As acute care surgeons, we have been using AI in the clinical realm and in our everyday lives for years. However, with the public release of conversational large language models, there has been a rapid increase in the uptake of AI, with numerous new products on the market and evolving conversations around what this means for the future of surgery. Given acute care surgeons are leaders in healthcare systems, interfacing with patients and impacting quality and safety through critical timepoints in the emergency department, operating rooms, wards, and intensive care units, it is imperative that we have an understanding of this technology, its potential for bias, its assessment and implementation, and its governance. In this review, we cover these critical concepts and aim to provide a framework for acute care surgeons to understand and evaluate the quality and safety of AI solutions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.