Transforming precision oncology with medical imaging foundation models

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Medical imaging foundation models are ushering in a new era for precision oncology. By integrating massive multimodal datasets and advanced AI algorithms, these models achieve unprecedented accuracy in early cancer screening, treatment planning, and prognosis prediction. Leveraging innovations such as self-supervised learning, transformer architectures, and contrastive learning, they enable deep integration of radiology, pathology, and genomics data. This shift from "single-task diagnosis" to "multi-dimensional intelligent analysis" represents a paradigm innovation, redefining how tumors are detected and managed, and paving the way for data-driven, personalized oncology care.

Precision oncology demands accurate, individualized treatment strategies—yet clinical decision-making remains challenged by tumor heterogeneity, patient diversity, and limited data integration. Traditional AI models, trained for single diseases or tasks, struggle to generalize across cancer types and clinical settings. Moreover, data privacy and computational limitations hinder large-scale model training and deployment. With the exponential growth of high-quality medical imaging and cancer registry data across China, opportunities to harness AI for cross-center, multimodal integration are rapidly expanding. Due to these challenges, there is an urgent need to develop medical imaging foundation models to advance intelligent, interpretable, and efficient precision oncology.

A research team from Guangdong Provincial People's Hospital and Southern Medical University published (DOI: 10.12290/xhyxzz.2025-0328) a review in Medical Journal of Peking Union Medical College Hospital (July 2025) outlining how medical imaging foundation models are transforming cancer precision medicine. The study systematically analyzes advances in large-scale data construction, algorithm optimization, and computational frameworks, emphasizing how multimodal integration and large language models (LLMs) enhance diagnostic accuracy and clinical interpretability. The paper also highlights emerging applications in early tumor screening, personalized therapy, and intelligent clinical decision support.

The authors identify three technological pillars underpinning the rise of medical imaging foundation models: (1) large-scale dataset construction, (2) algorithmic optimization, and (3) computational scalability. Standardizing imaging data across centers is essential, as differences in CT, MRI, and PET protocols often lead to heterogeneity. Privacy-preserving methods like federated learning and swarm learning mitigate data silos while enabling secure multi-institutional collaboration. Algorithmically, the combination of self-supervised learning, transformer attention mechanisms, and contrastive learning allows models to extract universal features from unannotated data, improving performance even for rare cancers. Lightweight architectures (e.g., TinyViT, MedSAM) and knowledge distillation techniques reduce hardware dependence, promoting broader clinical adoption. Clinically, imaging foundation models enhance cancer screening, triage optimization, and individualized therapy by integrating imaging, clinical notes, and electronic health records. Visualization tools like Grad-CAM and vision-language frameworks such as VQA improve interpretability, fostering physician trust and collaboration between AI and clinicians.

In the coming decade, medical imaging foundation models are expected to serve as the intelligent infrastructure of precision oncology. Their integration with hospital information systems will streamline workflows, automate risk alerts, and guide personalized treatment planning. As hospitals across China begin deploying models like DeepSeek, intelligent outpatient management and smart ward systems are becoming reality. Future progress will depend on cross-disciplinary collaboration, transparent regulation, and robust evaluation standards. Ultimately, these AI-driven models promise to shift clinical oncology from standardized protocols to patient-centered, data-driven, and adaptive care, redefining the future of cancer diagnosis and therapy.

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