Prostate Cancer(PCa) is one of the most frequently diagnosed cancers in men worldwide. Early detection plays a crucial role in lowering cancer specific mortality. Traditional screening tools such as the PSAs have sub optimal specificity sometimes leading to over diagnosis or missed aggressive disease. Artificial Intelligence (AI) and big-data analytics offer new avenues to improve early PCa detection. Significant advancements in machine learning (ML) , especially deep learning (DL) over recent years have shown promising results in enhancing diagnostic accuracy for prostate cancer.

A comprehensive literature search was performed (2010–2025) across PubMed, Ovid MEDLINE, and the Cochrane Library for studies on AI-driven digital biomarkers on early prostate cancer detection. Search was limited to peer-reviewed studies that had clinical validation. Pre-clinical and purely experimental studies were excluded. Following screening, over a dozen pertinent studies were identified ranging from applications in imaging, digital pathology to clinical biomarkers. Key data extracted were on AI methodologies, the digital biomarkers utilised, and diagnostic performance outcomes and the level of clinical validation.

The review presented studies investigating and discussing AI and big-data approaches in detecting cancer and grading Gleason scores. These included convolutional neural networks (CNNs) for image analysis to machine learning (ML) algorithms, Deep Learning (DL) models as well as utilisation of AI in multiparametric MRI as well as pathology image patterns and biomarkers like PSA trends. For ultrasound-based AI, clinical testing also showed clear benefits with studies showing AI models detecting substantially more cancers on TRUS images than human experts and reducing false positives. On PSA and blood biomarkers, a more refined risk stratification was found to ensure that fewer men with benign conditions would undergo biopsy compared to using PSA alone. Despite positive findings, the degree of validation varied. Many studies were retrospective, few studies have moved to prospective validation, and they confirm that AI tools can maintain performance in real-world practice and influence clinical processes. Only a minority of identified works were fully clinical trials, while others remain at the validation stage.

The review outcomes underscores the potential of artificial intelligence to revolutionise the diagnosis and management of prostate cancer. The incorporation of digital biomarkers, multi-omics data, and real-world evidence can provide AI-driven models with invaluable insights to improve early detection, risk stratification, and personalisation of treatment.