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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) shows significant promise in improving cancer detection accuracy, speed, and accessibility.
  • DL applications span imaging-based diagnostics and genomic analysis, impacting patient outcomes and mortality rates.

Purpose of the Study:

  • To comprehensively review recent research (2018-2024) on DL applications, opportunities, and challenges in oncology.
  • To explore emerging solutions for integrating DL into clinical practice.

Main Methods:

  • Systematic review of 1304 studies from PubMed and 115 from IEEE Xplore.
  • Analysis of current DL applications, challenges, and emerging solutions in cancer research.

Main Results:

  • DL offers transformative potential in oncology, improving diagnostic precision and patient treatment.
  • Key challenges include data quality, standardization, ethical/regulatory concerns, and model interpretability.

Conclusions:

  • Emerging solutions like federated learning, explainable AI, and synthetic data generation can address DL integration barriers.
  • Interdisciplinary collaboration and multimodal data approaches are crucial for advancing personalized cancer care through DL.