Artificial intelligence applications in prostate cancer

• Published in: Prostate cancer and prostatic diseases 2024-03, Vol.27 (1), p.37-45
• Main Authors: Baydoun, Atallah, Jia, Angela Y., Zaorsky, Nicholas G., Kashani, Rojano, Rao, Santosh, Shoag, Jonathan E., Vince, Randy A., Bittencourt, Leonardo Kayat, Zuhour, Raed, Price, Alex T., Arsenault, Theodore H., Spratt, Daniel E.
• Format: Article
• Language: English
• Subjects: 692/699/67; Algorithms; Artificial intelligence; Automation; Biomarkers; Biomedical and Life Sciences; Biomedicine; Cancer Research; Decision making; Histopathology; Medical imaging; Medical technology; Prostate cancer; Radiation therapy; Reproductive Medicine; Review Article; Workflow
• ISSN: 1365-7852; 1476-5608
• DOI: 10.1038/s41391-023-00684-0

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.