The Rise of Generalist Foundation Models and Quantum Computing in Oncology
Abstract
In the field of oncology, Artificial intelligence (AI) and deep learning (DL) are an essential component of decision-support. 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's 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…
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