Introduction
Emergency departments (EDs) operate under time pressure, generating multimodal data such as clinical conversations, triage notes, and discharge documents.
Recent advances in natural language processing (NLP), particularly pretrained transformers and large language models, have created new opportunities to support language and time-intensive stages of emergency care.
Yet existing surveys map clinical NLP across the broader hospital workflow or focus on specific tasks.
Methodology
This survey analyses 46 papers spanning the three phases of ED: triage, diagnosis, and disposition, covering tasks such as triage classification, clinical summarisation, automatic diagnosis, report generation, and discharge documentation.
We examine modelling paradigms, evaluation practices, and emerging benchmarks and shared tasks.
Findings
Across tasks, we identify common trends, including a shift from task-specific neural architectures to pretrained language models.
There is growing interest in interactive clinical systems.
Increasing attention is being paid to clinically grounded evaluation.
Challenges
Finally, we detail open challenges such as limited generalisability, noisy clinical inputs, and workflow constraints that inform future ED-NLP research.