Application of Large Language Models to the Diagnosis of Respiratory Diseases

Abstract

The implementation of large language models (LLM) using artificial intelligence can currently become extremely popular for solving various medical problems. Eight publicly available AI systems were prompted to make an otolaryngological diagnosis based on known symptoms obtained using the standard SNOT-22 medical questionnaire. The aim of the study was to find out to what extent modern AI systems can make a diagnosis without prior training. The results showed that most systems, with one exception, performed satisfactorily, achieving an accuracy of 70-80% compared to an accuracy of 84% achieved by a human specialist using various machine learning methods. The advantages and disadvantages of AI systems for medical diagnostics are discussed in the paper.