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Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review.

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    Explainable Artificial Intelligence (XAI) enhances AI model transparency, improving clinical adoption for COVID-19 diagnosis and staging. This systematic review details XAI techniques to boost trust and decision-making in healthcare AI.

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    Area of Science:

    • Biomedical Informatics
    • Artificial Intelligence in Medicine
    • Clinical Decision Support Systems

    Background:

    • Limited clinical impact of AI in COVID-19 diagnosis and staging due to lack of model transparency.
    • Need for explainable AI (XAI) to foster trust and facilitate adoption in clinical practice.
    • Importance of model interpretability for AI-driven healthcare solutions.

    Approach:

    • Comprehensive systematic review of Explainable Artificial Intelligence (XAI) techniques using PRISMA methodology.
    • Detailed introduction to common XAI methods, their applications, and evaluation strategies.
    • Exploration of traditional, modern, and advanced XAI models to illustrate technological evolution.

    Key Points:

    • XAI improves AI model performance and user trust, aiding clinical decision-making.
    • Effective evaluation of XAI results is crucial for maximizing the value of AI in healthcare.
    • XAI techniques offer specific examples and solutions for common challenges in AI model development.

    Conclusions:

    • XAI is essential for bridging the gap between AI development and clinical implementation in biomedicine.
    • This review provides best practice guidelines for AI model experimentation and adoption.
    • Promoting XAI adoption can significantly advance the use of AI in healthcare settings.