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Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.

Elena Fountzilas1, Tillman Pearce2, Mehmet A Baysal3

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Artificial intelligence (AI) and machine learning (ML) are revolutionizing precision oncology by analyzing complex data to improve cancer diagnosis and treatment. Challenges remain in data integration, algorithm development, and clinical implementation.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Medical Technology

Background:

  • Precision oncology aims to tailor cancer treatment to individual patients.
  • Advancements in artificial intelligence (AI) and machine learning (ML) offer new analytical capabilities.
  • Integrating multi-dimensional data is key to understanding tumor biology.

Purpose of the Study:

  • To explore the role of AI/ML in advancing precision oncology.
  • To highlight the potential of AI/ML in improving cancer diagnostics and therapeutics.
  • To identify current challenges hindering the widespread adoption of these technologies.

Main Methods:

  • Analysis of multi-dimensional, multiomic, spatial pathology, and radiomic data.
  • Application of AI/ML techniques for pattern recognition and pathway identification.
  • Generation of synthetic data, such as digital twins, for clinical trial design.

Main Results:

  • AI/ML enables a deeper understanding of tumor molecular pathways.
  • These technologies can optimize treatment selection for cancer patients.
  • Synthetic data generation can expedite clinical trial processes.

Conclusions:

  • AI/ML holds significant promise for the future of precision oncology.
  • Operational and technical challenges must be addressed for successful implementation.
  • Further research is needed on data sharing, generalizability, and clinical workflow integration.