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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Generative artificial intelligence (GAI), including large language models (LLMs) and generative computer vision, is increasingly influencing cancer research and clinical practice.
  • GAI applications span the entire continuum of cancer care, from initial diagnosis through treatment and patient follow-up.

Purpose of the Study:

  • To provide a comprehensive review of the current applications and future potential of GAI in oncology.
  • To specifically examine the role of GAI in urologic malignancies.

Main Methods:

  • This review synthesizes current literature on GAI applications in oncology.
  • Focuses on diagnostic, therapeutic, and follow-up care, including specific examples in urologic oncology.

Main Results:

  • GAI shows potential in improving cancer diagnosis through multimodal data integration and imaging interpretation.
  • In treatment, GAI can assist in clinical decision-making, therapy selection, and patient education.
  • Post-treatment, GAI can streamline administrative tasks, enhance follow-up, and monitor adverse events, with notable promise in urologic oncology for image analysis and outcomes research.

Conclusions:

  • GAI integration can enhance diagnostic accuracy, optimize treatment decisions, and improve clinical efficiency, thereby strengthening the patient-physician relationship.
  • Despite benefits, GAI's inherent variability requires human oversight, specialized models, physician training, and clear guidelines for safe and effective implementation in oncology.