A 2026 narrative review in Clinical and Experimental Emergency Medicine sets out a practical framework for evaluating fairness in emergency medicine applications of artificial intelligence, particularly large language models (LLMs). It says fairness includes equal performance across patient groups, equitable outcomes and allocation of clinical benefits, and procedural fairness throughout AI implementation.

The review groups risks into three layers. Data-level fairness concerns training, fine-tuning, or prompt data: biased clinical records, non-random missingness, and stigmatizing documentation can influence model performance. Output-level fairness asks whether AI-generated recommendations, predictions, translations, or instructions are equally accurate, safe, and useful across patient groups; subgroup errors can arise from aggregation, learning, or evaluation bias even when overall performance appears strong.

Human-interaction fairness concerns whether clinicians and patients share the benefits in practice. Inequities may arise when clinicians over-rely on or reject useful recommendations, or when patients cannot access, understand, or trust AI-mediated information. The review says clinicians should weigh safety, transparency, and fairness alongside overall accuracy when evaluating emergency medicine AI tools.

A 2026 narrative review in Clinical and Experimental Emergency Medicine sets out a practical framework for evaluating fairness in emergency medicine applications of artificial intelligence, particularly large language models (LLMs). It says fairness includes equal performance across patient groups, equitable outcomes and allocation of clinical benefits, and procedural fairness throughout AI implementation.

The review groups risks into three layers. Data-level fairness concerns training, fine-tuning, or prompt data: biased clinical records, non-random missingness, and stigmatizing documentation can influence model performance. Output-level fairness asks whether AI-generated recommendations, predictions, translations, or instructions are equally accurate, safe, and useful across patient groups; subgroup errors can arise from aggregation, learning, or evaluation bias even when overall performance appears strong.

Human-interaction fairness concerns whether clinicians and patients share the benefits in practice. Inequities may arise when clinicians over-rely on or reject useful recommendations, or when patients cannot access, understand, or trust AI-mediated information. The review says clinicians should weigh safety, transparency, and fairness alongside overall accuracy when evaluating emergency medicine AI tools.