Harnessing computational power for intelligent oncology in the age of large models: Status, challenges, and prospects

Abstract

The integration of large-scale foundation models (e.g., GPT series and AlphaFold) into oncology is fundamentally transforming both research methodologies and clinical practices, driven by unprecedented advancements in computational power. This review synthesizes recent advancements in the application of large language models to core oncological tasks, including medical imaging analysis, genomic interpretation, and personalized treatment planning. Underpinned by advanced computational infrastructures, such as GPU/TPU clusters, heterogeneous computing, and cloud platforms, these models enable superior representation learning and generalization across multimodal data sources. This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies, including mixed-precision training, memory optimization, and heterogeneous computing. Alongside these technical advancements, the review explores pressing challenges, including data heterogeneity, limited model interpretability, regulatory uncertainties, and the environmental impact of AI systems. Special emphasis is placed on emerging solutions, including green AI and edge computing, which offer promising approaches for low-resource deployment scenarios. Additionally, the review highlights the critical role of interdisciplinary collaboration, including oncology, computer science, ethics, and policy, to ensure that AI systems are not only powerful but also transparent, safe, and clinically relevant. Finally, the review outlines potential avenues for future research aimed at developing robust, scalable, and human-centered frameworks for intelligent oncology.

Ähnliche Arbeiten