AI applications in this space include analysis of non-invasive imaging modalities, such as multiparametric Magnetic Resonance Imaging (MRI) and Ultrasound, which enhance diagnostic precision. AI models also concatenate serum biomarkers and histopathological analysis to distinguish BPH from prostate cancer (PC), offering high accuracy rates. Furthermore, AI aids in predicting patient outcomes post-treatment, supporting personalized medicine, and optimizing therapeutic strategies. AI has demonstrated potential in differentiating BPH from PC through advanced imaging and predictive models, improving diagnostic accuracy, and reducing the need for invasive procedures. Despite promising advancements, challenges remain in integrating AI into clinical workflows, establishing standard evaluation metrics, and achieving cost-effectiveness. Here, we underscore the potential of AI to improve patient outcomes, streamline BPH management, and reduce healthcare costs, especially with continued research and development in this transformative field.
Keyphrases
- artificial intelligence
- magnetic resonance imaging
- prostate cancer
- benign prostatic hyperplasia
- healthcare
- lower urinary tract symptoms
- high resolution
- machine learning
- decision making
- deep learning
- computed tomography
- radical prostatectomy
- metabolic syndrome
- skeletal muscle
- risk assessment
- antiretroviral therapy
- combination therapy
- ultrasound guided
- data analysis
- contrast enhanced ultrasound