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 progress 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 graphics processing unit/tensor processing unit 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

such as data heterogeneity

limited model interpretability

regulatory uncertainties

and the environmental impact of artificial intelligence (AI) systems. Special emphasis is placed on emerging solutions

encompassing green AI and edge computing

which offer promising approaches for low-resource deployment scenarios. Additionally

the review highlights the critical role of interdisciplinary collaboration among 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.