Comprehensive Summary
Murad et al. examined the applications of artificial intelligence in the diagnosis, treatment, and management of benign prostatic hyperplasia (BPH). AI models, such as machine learning (ML) and deep learning (DL), have been used in imaging and pathology to improve diagnostic precision and treatment planning. For instance, MRI-based AI systems displayed high accuracy when it came to separating BPH from prostate cancer. AI also has been used to analyze biopsy results and predict surgical success rates. Regardless, it is notable that most of today’s research is focused on distinguishing BPH from prostate cancer rather than improving diagnosis and care for BPH itself. The authors also stated that small sample sizes and a lack of standardized reporting make it difficult to apply these models in real clinical settings.
Outcomes and Implications
This paper highlights the growing relevance of AI in urologic research and practice, especially for improving diagnostic consistency and management in BPH. To make this possible, future research will need larger and more diverse studies, alongside more reliability and validity within its results. Combining different types of data, such as imaging and lab tests, could allow for AI tools to be more reliable and practical in future clinical practice. AI has the potential of improving noninvasive diagnosis of BPH and predicting patient outcomes once the tools are properly developed and tested.