Explainable Artificial Intelligence for Reducing the Global Cancer Burden

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a powerful approach for reducing the global cancer burden by enhancing transparency, trust, and clinical impact in oncology. Traditional AI models often operate as “black boxes” that provide results without clear reasoning, which limits their acceptance in clinical practice. XAI addresses this issue by offering interpretable insights into how predictions are generated, allowing clinicians to verify results against established medical knowledge and patient-specific conditions. In cancer diagnosis, XAI can show which imaging characteristics, genomic alterations, or clinical variables most strongly influence an outcome, supporting earlier and more reliable detection. This interpretability reduces the risk of misdiagnosis and increases physician confidence in AI-assisted tools. In treatment planning, XAI helps identify relevant patterns within complex datasets such as tumor genomics and patient records. This transparency clarifies why certain therapies may be more effective for particular individuals and advances the goals of precision medicine. Beyond individual care, XAI can benefit cancer control efforts at a global level. When screening and risk prediction systems are transparent, they are more likely to be trusted, adopted, and regulated in diverse healthcare settings, including those with limited resources. Clear explanations support policymakers, regulatory agencies, and clinicians in ensuring that AI tools are applied ethically and equitably. By combining predictive accuracy with interpretability, XAI provides not only technological advancement but also a pathway to improve equity in cancer care. Its integration can accelerate early detection, optimize therapies, and ultimately reduce the worldwide cancer burden.