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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Modernizing Colorectal Cancer Care With Artificial Intelligence: Real-Time Detection, Radiomics, and Digital

Elmoatazbellah Nasr1, Zaid Al-Hamid2, Mina H Younan3

  • 1General Surgery, Calderdale and Huddersfield NHS Foundation Trust, Huddersfield, GBR.

Cureus
|October 24, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances colorectal cancer (CRC) detection, diagnosis, and treatment planning. Implementing AI requires robust data, validation, and integration for improved patient outcomes.

Keywords:
artificial intelligence (ai)colonoscopycolorectal cancer (crc)computer-aided detection (cade)deep learningdigital pathologyradiomics

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computational Biology

Background:

  • Colorectal cancer (CRC) presents a significant global health challenge, necessitating advancements in early detection and personalized treatment strategies.
  • Artificial intelligence (AI) is emerging as a transformative technology within the CRC care pathway.

Purpose of the Study:

  • To review the current and potential applications of AI in improving colorectal cancer detection, molecular profiling, and treatment decision-making.
  • To highlight the requirements for successful AI implementation in clinical practice for CRC management.

Main Methods:

  • Review of current literature on AI applications in CRC, including computer-aided detection, digital pathology, medical imaging analysis (CT/MRI), and omics-driven models.
  • Analysis of AI's role in enhancing lesion detection, molecular subtyping, risk stratification, staging, and treatment response prediction.

Main Results:

  • AI-powered computer-aided detection systems demonstrably improve adenoma detection rates and reduce miss rates in colonoscopies.
  • Digital pathology using AI can predict microsatellite instability from routine slides, aiding in molecular triage.
  • AI enhances medical imaging interpretation (CT/MRI) through segmentation, radiomics, and nodal staging, improving diagnostic accuracy and treatment response prediction.
  • AI models utilizing blood and genomic data show promise for noninvasive screening and prognostic assessment in CRC.

Conclusions:

  • AI offers substantial potential to revolutionize colorectal cancer care by improving diagnostic accuracy, personalizing treatment, and enhancing patient outcomes.
  • Successful translation of AI into clinical practice hinges on addressing challenges related to data quality, validation, interpretability, workflow integration, and ethical governance.
  • Future efforts should focus on multicenter prospective studies and robust implementation frameworks to ensure AI's equitable and cost-effective use in improving CRC management.