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Related Experiment Video

Updated: Sep 16, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

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Artificial intelligence in prostate cancer.

Wei Li1, Ruoyu Hu1, Quan Zhang1

  • 1Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.

Chinese Medical Journal
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning (DL) are revolutionizing prostate cancer (PCa) care. These technologies enhance early diagnosis, personalized treatment, and prognosis prediction, improving patient survival rates.

Keywords:
Artificial intelligenceFoundation modelImagingPathologyProstate cancerWhole-slide image

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Prostate cancer (PCa) is a leading global malignancy in men.
  • Effective PCa management relies on early diagnosis, tailored treatments, and accurate prognosis.
  • Advancements in AI, especially deep learning (DL), offer significant potential in PCa care.

Purpose of the Study:

  • To review the clinical applications of AI in prostate cancer.
  • To highlight recent progress in AI for PCa diagnosis, treatment, and prognosis from pathological and imaging viewpoints.
  • To discuss current challenges and future directions for AI in clinical PCa settings.

Main Methods:

  • Review of recent literature on AI applications in prostate cancer.
  • Focus on deep learning algorithms and foundation models.
  • Analysis of AI's role in pathological and imaging data interpretation.

Main Results:

  • AI, particularly DL, shows substantial progress in assisting PCa diagnosis, treatment, and prognosis.
  • Foundation models are transforming AI integration into clinical practice.
  • AI demonstrates potential across both pathological and imaging domains in PCa.

Conclusions:

  • AI holds significant promise for improving prostate cancer patient outcomes.
  • Addressing current challenges is key for successful clinical integration of AI in PCa.
  • This review serves as a reference for AI's future role in prostate cancer management.