Towards Causal Explainable AI in Cancer Diagnosis: Advances, Challenges, and Future Directions

Breakthroughs in Artificial Intelligence (AI) are reshaping oncology, facilitating early cancer detection, more accurate diagnosis, and personalized treatment. However, its clinical adoption is hindered by black-box models' opacity, raising concerns about reliability, accountability, and ethical implications. Although explainable AI (XAI) helps mitigate these issues, it often faces challenges, such as low fidelity, fairwashing, and vulnerability to spurious correlations and biases. Causal eXplainable AI (CXAI) aims to address these shortcomings by leveraging causal inference, thereby improving model robustness, fairness, and clinical relevance. While recent studies have explored causality, explainability, and AI in healthcare, they largely remain conceptual and lack a comprehensive synthesis in oncology. This paper fills this gap by providing the first comprehensive review of CXAI for cancer diagnosis. We summarize current applications across multiple cancer types, identify pressing challenges, and propose future directions. Our findings highlight CXAI's potential to make AI-driven oncology more trustworthy, transparent, and effective.Clinical relevance-Causal Explainable AI (CXAI) significantly enhances clinical decision-making by capturing causal relations in medical data, thus improving predictive accuracy and aligning AI insights with clinical reasoning. By increasing transparency and trust, CXAI facilitates AI adoption in oncology, enabling informed diagnostic decisions and personalized patient care.