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Related Concept Videos

Introduction to Language of Pathophysiology l01:25

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like pain), laboratory test...
Introduction to Language of Pathophysiology ll01:17

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

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Clinical Application of Large Language Models in Generating Pathologic Images.

Lingxuan Zhu1,2, Yancheng Lai3, Na Ta4

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

JCO Clinical Cancer Informatics
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) model DALL·E 3 shows promise in generating synthetic prostate cancer (PCa) pathology images for education. While valuable for teaching, limitations in fine detail require careful ethical integration into pathology practices.

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Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Medicine
  • Medical Education Technology

Background:

  • Prostate cancer (PCa) diagnosis relies on accurate histopathology.
  • Generating diverse and representative training datasets is crucial for medical education.
  • Current resources for pathology training may lack sufficient variety in Gleason grades.

Purpose of the Study:

  • To evaluate the capability of DALL·E 3 to create synthetic prostate cancer (PCa) images.
  • To assess the realism and accuracy of AI-generated images across different Gleason grades.
  • To explore the utility of synthetic images for enhancing pathology education and research.

Main Methods:

  • DALL·E 3 was used to generate 30 synthetic PCa images across various Gleason grades.
  • Images were created based on standard Gleason pattern descriptions.
  • Nine uropathologists assessed image realism and accuracy against actual H&E-stained slides.

Main Results:

  • AI-generated images received average realism and representativeness scores of 6.04 and 6.17, respectively.
  • Significant variations in scores were observed across Gleason patterns (P < .05).
  • Gleason 5 images scored highest, accurately reflecting key pathologic features, though fine nuclear detail was limited.

Conclusions:

  • DALL·E 3 demonstrates potential for generating customized pathology images to expand educational resources.
  • Ethical considerations, including data falsification risks, necessitate responsible AI implementation.
  • Collaboration between AI developers and pathologists is vital for ethical integration into pathology.