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

Hideyuki Shimizu1, Keiichi I Nakayama1

  • 1Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.

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Artificial intelligence (AI) and deep learning are revolutionizing cancer research by solving complex problems. Further development and resources are crucial for widespread AI application in oncology.

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Area of Science:

  • Biomedical research
  • Oncology
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) has significantly advanced biomedical problem-solving over the last decade.
  • Deep learning, a flexible AI subfield, excels at automatic feature extraction and is increasingly used in cancer research.

Purpose of the Study:

  • To review recent applications of AI and deep learning in oncology.
  • To highlight AI's success in solving previously intractable cancer research problems.
  • To discuss challenges and resources for broader AI adoption in cancer research.

Main Methods:

  • Review of recent literature on AI and deep learning in cancer research.
  • Identification of successful AI applications in basic and clinical oncology.
  • Analysis of obstacles to widespread AI implementation.

Main Results:

  • AI, particularly deep learning, has demonstrated remarkable success in addressing complex biomedical and cancer-related challenges.
  • Numerous examples showcase AI's ability to solve previously unsolvable problems in oncology.
  • Key resources and datasets are available to facilitate AI-driven cancer research.

Conclusions:

  • AI and deep learning offer powerful tools for advancing oncology.
  • Overcoming implementation obstacles is essential for realizing AI's full potential in cancer research.
  • Continued innovation in AI approaches will drive significant future insights in the field.