Fig. 2. Pie charts showing the proportions of ML models used in various laboratory medicine fields based on a literature review of laboratory medicine studies involving ML applications from February 2014 to April 2024. Numbers in parentheses indicate the number of published articles related to ML in each field. The frequencies at which various ML models were used in (A) diagnostic hematology, (B) clinical chemistry, (C) clinical microbiology, (D) molecular diagnostics, (E) transfusion medicine, and (F) diagnostic immunology are shown.
Abbreviations: CNN, convolutional neural network; DBN, deep belief network; DNN, deep neural network; HCA, hierarchical cluster analysis; LLM, large language model; LR, logistic regression; LSTM, long short-term memory; ML, machine learning; MLP, multilayer perceptron; N/S, not specified; PLS-DA, partial least squares-discriminant analysis; RF, random forest; RNN, recurrent neural network; SVM, support vector machine; UMAP, uniform manifold approximation and projection; XGB, extreme gradient boosting.

Ann Lab Med 2025;45:22~35 https://doi.org/10.3343/alm.2024.0354