As medical science and technology advance toward the "big data" era, a multi-dimensional dataset pertaining to medical diagnosis and treatment becomes available for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and have a high degree of redundancy. As a result, extensive data processing is widely recommended before feeding the dataset into the mathematical model. Artificial intelligence techniques, such as machine learning and deep learning algorithms based on artificial neural networks and their variants, are being used in this context to generate a precise and cross-sectional illustration of clinical data. Datasets derived from prostate-specific antigen, MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring in prostate cancer patients.
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