Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration - Takeaways - MDSpire

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration

  • By

  • Dimitris Fotopoulos

  • Ioannis Ladakis

  • Dimitrios Filos

  • Pedro A Moreno-Sánchez

  • Mark van Gils

  • Ioanna Chouvarda

  • May 20, 2026

  • 0 min

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  • 1

    Cancer is a leading cause of mortality, and AI can enhance diagnostic accuracy and treatment planning in cancer care.

  • 2

    The EU AI Act of 2024 classifies AI systems for cancer diagnosis as high-risk, necessitating transparency and human oversight.

  • 3

    Explainable AI (xAI) aims to make AI decision-making processes transparent, which is crucial for trust and clinical adoption.

  • 4

    Real-world implementation of AI in oncology faces challenges, including workflow integration and the need for clinician training.

  • 5

    A comprehensive understanding of xAI methods in cancer imaging is lacking, with limited validation and evaluation of their effectiveness.

Original Source(s)

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration

  • by Dimitris Fotopoulos, Ioannis Ladakis, Dimitrios Filos, Pedro A Moreno-Sánchez, Mark van Gils, Ioanna Chouvarda

  • May 20, 2026

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