Eye tracking is a non-invasive technology that facilitates the real-time monitoring of eye movements. It offers insights into visual behavior and cognitive processes. The analysis of eye movement data involves two key components: fixations, (periods of stable gaze), and saccades (rapid shifts in focus between points of interest). This brief overview evaluates significant recent studies conducted between 2018 and 2023 that employ eye tracking for task related to the medical classification. Main algorithms that process eye movement data are used to extract features like pupil positions, fixation duration, and saccade characteristics. Our analysis showed that investigators use machine learning algorithms such as support vector machines, k-nearest neighbors, random forest, and convolutional neural networks for distinguishing normal and pathological states, typically in binary classification tasks, measured by accuracy and AUC. Generally, the size of the datasets is limited. However, authors achieved reliable classification results, ranging from 52% to 95%. As technology continues to evolve, the integration of eye tracking and machine learning offers a promising path toward enhancing our understanding of cognitive processes and medical diagnostic capabilities.