A STUDY ON AI MODELS FOR MULTI-DISEASE DIAGNOSIS USING STRUCTURED CLINICAL DATA

Abstract

In this work, I examine how artificial intelligence can help doctors understand the huge amounts of clinical information that accompanies allergic, neurological and cardiovascular diseases. At first glance, these three areas are almost unrelated to each other, but in fact they have an important common feature: each of them forms a large amount of structured data - laboratory parameters, examination results, clinical records, symptom dynamics and many small details that are easy to overlook in real practice. It is difficult for a person to process such information immediately, but for AI, the search for patterns in complex data sets is a familiar task.

In our study, I analyse how different models of artificial intelligence - from classical machine learning methods to deep neural networks - apply to three groups of diseases. In allergology, AI helps to recognise the types of sensitisation and predict the reaction to therapy in advance. In neurology, models are able to analyse EEG, brain images and slow cognitive changes that develop over the years. In cardiology, AI supports doctors in detecting arrhythmias, assessing the functional state of the heart and predicting risks. In all three areas, predictive algorithms allow you to notice changes earlier and select treatment more accurately and individually.

At the same time, I emphasise that artificial intelligence is not a magical replacement for a doctor. Its effectiveness depends on the quality of the data, and the application requires caution and control by specialists. But in general, it becomes clear: AI is gradually becoming an important tool of modern medicine. It does not take the job away from the doctor, but helps to see the full picture, combining data and facilitating timely, balanced decisions.