Transformer-based models have transformed the branch of Natural
Language Processing (NLP), significantly developing healthcare applications. This
abstract examines the advancements and effects of these models in healthcare,
emphasizing their strengths, obstacles, and future possibilities. Notable transformer
architectures such as BERT and GPT, along with their variants, have greatly improved
the proficiency of NLP systems in comprehending and producing human language.
These models are particularly effective for tasks like named entity recognition,
relationship extraction, and clinical text generation, which are essential for managing
and interpreting large volumes of healthcare information. Their ability to be pre-trained
on extensive datasets and fine-tuned with specialized medical information has allowed
transformers to attain cutting-edge performance in medical coding, Electronic Health
Record (EHR) analysis, and patient sentiment evaluation. Implementing transformerbased models in the healthcare sector has enhanced diagnostic precision, provided
more tailored treatment suggestions, and improved patient engagement through
chatbots and virtual assistants. For instance, models like BioBERT and ClinicalBERT,
specifically fine-tuned on biomedical and clinical literature, have demonstrated notable
advancements in interpreting and predicting medical conditions based on clinical notes.
Nevertheless, several challenges persist, including concerns about data privacy, the
necessity for extensive and varied medical datasets, and the interpretability of complex
model predictions. Tackling these issues requires ongoing research on model
transparency, privacy-preserving methods, and strategies to address the imbalance and
variability in healthcare data. Looking ahead, transformer-based models are set to
further revolutionize healthcare by facilitating more accurate and scalable solutions.
Improvement in model design, training approaches, and domain-specific
customizations will be vital for realizing their full potential to enhance healthcare
outcomes and operational efficiency.
Keywords: Diagnostic precision, Electronic health records, Healthcare, Model predictions, Patient engagement, Transformer architectures, Virtual assistants.
