The Role of Quantum Computing in Transforming Healthcare: A Systematic Literature Review of Quantum Machine Learning Applications in Oncology
Abstract
Quantum Machine Learning (QML) is an emerging field with significant potential to transform oncology. This systematic literature review evaluates the current landscape of QML applications in cancer research, analyzing 21 primary research articles, selected from 410 initially identified, published between 2020 and 2024. The review categorizes QML applications, assesses evidence for quantum advantage, identifies technical challenges, and outlines future directions. Findings indicate robust research activity, particularly in China and India, with hybrid quantum-classical algorithms showing promise in simulated environments for tasks like diagnosis and prediction, especially with small or imbalanced datasets. However, translation into clinical practice remains constrained by hardware limitations and the lack of rigorous real-world validation, resulting in a persistent gap between simulation and clinical application. In addition, most studies adopt quantitative experimental approaches, with limited exploration of qualitative perspectives such as usability, interpretability, and workflow integration. To address these gaps, future research should pursue standardized frameworks, mixed-method evaluation protocols, and cross-disciplinary collaboration between quantum scientists, clinicians, and implementation researchers. Such efforts would provide robust, comparable evidence of clinical utility, ensure trustworthiness in deployment, and accelerate the transition of QML from…
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