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Related Experiment Video

Updated: Oct 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists.

Jose P Amorim, Pedro H Abreu, Alberto Fernandez

    IEEE Reviews in Biomedical Engineering
    |November 30, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This guide explains how artificial intelligence (AI) decision-support systems work in oncology. It addresses the challenge of AI interpretability, crucial for clinical adoption in cancer care.

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    Area of Science:

    • Artificial Intelligence in Oncology
    • Clinical Decision Support Systems
    • Machine Learning Interpretability

    Background:

    • Healthcare generates vast patient data, driving AI adoption in oncology.
    • Deep learning decision-support systems are approved but face adoption barriers.
    • Lack of interpretability hinders the clinical integration of AI in cancer care.

    Approach:

    • Presents a guide for oncologists on AI decision-making processes.
    • Illustrates strategies for explaining AI model predictions.
    • Reviews literature on deep learning interpretability in oncology (Jan 2014-Sep 2020).

    Key Points:

    • Focuses on breast, skin, and brain cancers (over 60% of reviewed studies).
    • Many studies explain AI predictions by highlighting tumor characteristics (e.g., dimension, shape).
    • Multilayer perceptrons and convolutional neural networks are common computational methods.

    Conclusions:

    • Interpreting AI decisions is vital for clinical trust and adoption.
    • Maintaining AI performance while ensuring interpretability remains a significant challenge.
    • Further research is needed to bridge the gap between AI capabilities and clinical needs in oncology.