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TRUVACE RECORD VERSION
record: TRV-2026-1559
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T10:41:24.636041Z
status: published
lens: p_space
sector: health
headline: Large Language Models in the Emergency Department: Fairness Implications for Clinicians
dek: Applications of artificial intelligence (AI), particularly large language models (LLMs), are rapidly expanding in emergency medicine, but concerns remain regarding their fairness across diverse patient populations. Fairness extends beyond model accuracy and includes equal performance across patient groups, equitable outcomes and allocation of clinical benefits, and procedural fairness throughout the lifecycle of AI implementation. This narrative review aims to provide a practical framework for understanding and…
gain_title: (none)
problem_title: AI and LLM tools in emergency care can produce subgroup-specific errors and unequal benefits across patient groups, even when overall model performance appears strong.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: AI and LLM tools in emergency care can produce subgroup-specific errors and unequal benefits across patient groups, even when overall model performance appears strong.
problem_evidence: subgroup-specific errors in recommendations, predictions, translations, or instructions may arise from aggregation, learning, or evaluation bias, even when overall model performance appears strong | Biased clinical data, including non-random missingness and stigmatizing documentation, can influence model performance
quick_read: A narrative review published in Clinical and Experimental Emergency Medicine examines how large language models, which are spreading in emergency medicine, may perform unevenly across patient populations. It defines fairness broadly, covering equal performance across groups, equitable outcomes and allocation of benefits, and fair procedures across the AI lifecycle.

The review matters because emergency clinicians may judge tools mainly by overall accuracy, which can conceal unequal safety or usefulness for particular groups. The source offers a framework rather than measured outcomes, so how often these problems occur in real emergency departments is not established by the text.
limitation: The source is a narrative review offering a conceptual framework, and it does not report measured rates of unfair performance or outcomes in emergency departments.
tag: Evidence-backed problem
key_points: Fairness is split into three layers: data-level, output-level, and human-interaction fairness. | Data-level risks include non-random missingness and stigmatizing documentation in the clinical data used to train, fine-tune, or prompt AI systems. | Output-level risks include subgroup-specific errors in recommendations, predictions, translations, or instructions. | Human-interaction risks include clinicians over-relying on or rejecting useful AI recommendations, and patients being unable to access, understand, or trust AI-mediated information. | Clinicians are advised to weigh safety, transparency, and fairness alongside overall model accuracy.
rundown: The authors present a practical framework for understanding and evaluating fairness in emergency medicine uses of AI and LLMs. Fairness is treated as more than accuracy, spanning equal group performance, equitable outcomes, and procedural fairness.

At the data level, the review says biased clinical data can shape model behavior. Examples named are non-random missingness and stigmatizing documentation, which affect data used to train, fine-tune, or prompt systems.

At the output level, errors may arise from aggregation, learning, or evaluation bias. The review says such errors can affect translations and instructions as well as predictions and recommendations.

At the human-interaction level, inequities can persist even when data and outputs are fair. Clinician over-reliance or rejection of useful recommendations, and patient barriers to access, understanding, or trust, can all produce uneven benefit.
sources:
- peer_reviewed | Clinical and Experimental Emergency Medicine | https://doi.org/10.15441/ceem.26.409 | 2026-10-15
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