Transformer-based Clinical Report Generation from Stroke Electronic Health Records (EHRs): A Comprehensive Review

Abstract

Structured Electronic Health Records (EHRs) provide a valuable source for generating clinical narrative reports that are increasingly automated in this rapidly evolving field of medical AI, especially in stroke care where timely documentation is vital. Complex multimodal data comes with stroke patients in the form of lab results, neurological scores (e.g., NIHSS), imaging results, medication histories, and structured diagnostic codes. Converting structured information to coherent clinical narratives like radiology reports or discharge summaries is still a difficult natural language generation (NLG) problem. In recent years, various Transformer-based architectures have emerged, such as encoder–decoder models like T5 and BART, and decoder-only large language models (LLMs) like GPT, LLaMA, and Phi, which have greatly enhanced the generation of medical texts. However, the generation of a stroke-specific EHR-to-report is not widely discussed, as datasets are limited, there is a lack of standardized benchmarks, and there is little clinical validation. This is a systematic review of literature from 2015–2026 related to Transformer-based clinical report generation for stroke patients. Datasets, architectures, evaluation metrics, baseline models, and clinical applications are discussed. We also list some limitations such as risk of hallucination, poor generalization and lack of paired datasets for strokes. Lastly, we suggest future directions such as multimodal learning, retrieval-augmented generation, and clinically-based evaluation frameworks.