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Transformer-based architectures have significantly advanced clinical natural language processing by improving the capture of contextual relationships in unstructured electronic health records compared to earlier recurrent and convolutional models, with domain-specific variants such as ClinicalBERT and BioBERT designed to better handle clinical terminology, abbreviations, and specialized language, thereby improving information extraction performance, although the relative impact of different pre-training strategies remains insufficiently synthesized and requires systematic evaluation of corpus selection and fine-tuning approaches; this systematic review mapped studies focusing on pre-training corpora, fine-tuning methods, and named entity recognition performance across entity types such as medications, diseases, procedures, laboratory tests, and social determinants of health, using PRISMA-guided methods and searches across PubMed, ACL Anthology, arXiv, and IEEE Xplore, identifying 32 eligible studies from 1,247 records; findings showed that ClinicalBERT, BioBERT, and PubMedBERT were the most frequently evaluated models, pre-trained on datasets such as MIMIC-III, PubMed abstracts, and mixed biomedical corpora, with consistent evidence that domain-specific pre-training outperforms general-domain BERT models on benchmarks like i2b2 and n2c2 despite variation across entity types and fine-tuning strategies, while clinical pre-training on large EHR corpora improves named entity recognition and optimized fine-tuning approaches such as lower learning rates and data augmentation further enhance performance, particularly for medications and diseases, underscoring the importance of domain adaptation and the need for more standardized evaluation protocols in clinical NLP research.