Quantum-driven precision oncology: The future of cancer detection

Abstract

Cancer remains a major global health challenge, necessitating the development of accurate, scalable, and clinically reliable diagnostic approaches. Artificial intelligence (AI) has significantly enhanced cancer detection through advanced analysis of medical imaging, histopathology, and multi-omics data. However, conventional AI models face limitations related to computational scalability, optimization complexity, and data heterogeneity. Quantum computing introduces a fundamentally different computational paradigm based on superposition, entanglement, and probabilistic state evolution. The integration of quantum computing with machine learning referred to as Quantum Artificial Intelligence (QAI) has emerged as a promising approach to address these challenges. This review critically evaluates the role of QAI in cancer detection through a systematic analysis of literature published between 2015 and 2026. The findings suggest that QAI can enhance feature representation, accelerate optimization processes, and improve predictive performance. Hybrid quantum-classical models show potential in tumor classification, multi-omics integration, and real-time clinical decision-making. Despite these advances, significant challenges remain, including hardware limitations, lack of large-scale validation, and ethical concerns. Overall, QAI represents a promising yet emerging paradigm with potential to transform early cancer detection and precision oncology. This review uniquely synthesizes quantum-AI approaches in oncology, emphasizing hybrid quantum-classical models as the most practical translational pathway for clinical adoption.

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