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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence in oncological imaging screening.

Zhihong Guo1, Meng Xu, Chaoliang Zhong

  • 1Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.

International Journal of Surgery (London, England)
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) in oncological imaging enhances early cancer detection across various modalities. Addressing challenges like dataset bias and regulatory needs is crucial for equitable AI implementation in cancer care.

Keywords:
Artificial Intelligencecancer screeningclinical applicationdeep learningimaging modalities

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Global cancer burden is rising, necessitating improved early detection methods.
  • Artificial Intelligence (AI) offers transformative potential in oncological imaging for cancer screening.
  • Current imaging modalities face limitations in early and accurate cancer detection.

Purpose of the Study:

  • To review the evolution and clinical applications of AI in oncological imaging for cancer detection.
  • To highlight advancements in AI-driven early cancer screening across diverse imaging techniques.
  • To address challenges and propose solutions for effective AI implementation in medical imaging.

Main Methods:

  • Comprehensive literature review of AI applications in oncological imaging.
  • Analysis of AI's role in ultrasound, X-ray, CT, MRI, and endoscopy for cancer detection.
  • Examination of challenges including dataset bias, regulatory frameworks, and integration barriers.

Main Results:

  • AI demonstrates significant advancements in early cancer detection across multiple imaging modalities.
  • Key challenges identified include data bias, regulatory hurdles, and technical integration.
  • Standardized datasets, explainable AI, and equitable implementation are vital for success.

Conclusions:

  • AI is poised to revolutionize cancer care through earlier, more accurate detection and personalized risk stratification.
  • Addressing implementation challenges is essential for realizing AI's full potential in improving patient outcomes.
  • Integrating AI with clinical validation and ethical governance will enhance cancer screening and treatment.