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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Updated: Mar 31, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Agentic AI for Prostate Cancer: A Vision for Multimodal Clinical Intelligence.

N Papanikolaou1, A R Padhani2, F Sanguedolce3

  • 1Clinical Computational Imaging Group, Champalimaud Research, Lisbon, Portugal.

European Urology Oncology
|March 29, 2026
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Summary
This summary is machine-generated.

Agentic AI systems are being developed to integrate diverse artificial intelligence (AI) tools for prostate cancer care. These systems aim to enhance clinical decision-making by synthesizing complex patient information more effectively.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) shows promise in prostate cancer diagnostics and treatment planning.
  • The integration of AI into routine clinical decision-making for prostate cancer remains limited.
  • A need exists for tools that can effectively synthesize diverse clinical data to support oncologists.

Purpose of the Study:

  • To introduce and describe the concept of Agentic AI systems.
  • To highlight the role of Agentic AI systems as an orchestration layer for various AI agents.
  • To propose Agentic AI systems as a solution for enhancing clinical decision-making in prostate cancer care.

Main Methods:

  • Development of Agentic AI systems.
  • Integration of multiple AI agents within an orchestration layer.
  • Focus on synthesizing diverse clinical information for decision support.

Main Results:

  • Agentic AI systems are conceptualized as a method to bridge the gap between AI advancements and clinical practice.
  • These systems are designed to process and synthesize varied data inputs.
  • The proposed systems aim to provide effective support for clinicians' decision-making processes.

Conclusions:

  • Agentic AI systems represent a novel approach to leveraging AI in clinical oncology.
  • Effective synthesis of clinical information is crucial for improving AI's impact on patient care.
  • Further development of Agentic AI systems is expected to enhance clinical decision-making in prostate cancer management.