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However, traditional narrow-AI models have significant limitations in clinics with respect to narrow, task-specificity (TS), high data requirement and interpretability. Moreover, the absence of common clinical criteria and the lack of doctors in the co-development of explainable AI (XAI) have undermined the implementation causing conflict with general data protection regulation (GDPR), trust and ethical integration requirements. The application of AI\/DL tools in clinics are often constrained by TS, heavy dependency on hyperparameter tuning and large data volume. This review fills in these gaps by creating a cohesive framework that links problem-driven clinical demands with emerging technologies, specifically, the convergence of Generalist Medical AI (GMAI) and Quantum Oncology (QO). Although GMAI\u2019s use foundation models, have high computational requirements and have inherent complexity in their validation pipelines, they are designed to be based on self-supervised learning from multimodal data to address a range of downstream clinical tasks. This review aims to critically discuss the potential of quantum computing (QC) to augment GMAI for more efficient data processing, medical imaging, drug discovery, and genomic analysis, owing to the inherent strengths of quantum superposition and entanglement that surpass the capabilities of classical AI\/DL systems. Structural and technical trade-offs of this paradigm change are also discussed. We also give recommendations for safe bedside translation by facilitating a common assessment through clinician in the loop design, the CLAIM checklist, and the framework FUTURE-AI. Finally, this analysis outlines oncology and quantum convergence into quantum oncology (QO). 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