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The need for skilled medical support is growing in the era of digitalhealthcare. This research presents an innovative strategy, utilizing the RuBERTmodel, for categorizing user inquiries in the field of medical consultationwith a focus on expert specialization. By harnessing the capabilities oftransformers, we fine-tuned the pre-trained RuBERT model on a varied dataset,which facilitates precise correspondence between queries and particular medicalspecialisms. Using a comprehensive dataset, we have demonstrated our approach'ssuperior performance with an F1-score of over 92%, calculated through bothcross-validation and the traditional split of test and train datasets. Ourapproach has shown excellent generalization across medical domains such ascardiology, neurology and dermatology. This methodology provides practicalbenefits by directing users to appropriate specialists for prompt and targetedmedical advice. It also enhances healthcare system efficiency, reducespractitioner burden, and improves patient care quality. In summary, oursuggested strategy facilitates the attainment of specific medical knowledge,offering prompt and precise advice within the digital healthcare field.

Transformer-based classification of user queries for medical consultancy with respect to expert specialization