Artificial intelligence applications in prostate cancer

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1365-7852
1476-5608
DOI:10.1038/s41391-023-00684-0