Analytical comparison of linguistic models for medical text processing

Within the scope of this article, an analytical comparison of popular language models for medical text processing is carried out. The covered models are MedCAT, ScispaCy, BioBERT and different versions of GPT. Evaluation is done according to a number of criteria: accuracy of information extraction, context understanding, universality of application, speed of information processing, presence of robustness to data noise and level of interpretability of results. The results of the research found that the MedCAT model has the highest scores and is more suitable for application in medical tasks. The study also emphasises the importance of integrating popular language models into processes related to medical information processing in order to improve the efficiency and quality of medical care.