Foundation Models for Medical Imaging: Large-Scale Pretraining for Universal Diagnostic Systems

Abstract

The rapid advancement of artificial intelligence in medical imaging has led to significant improvements in diagnostic accuracy; however, most existing systems remain narrowly designed for specific tasks, datasets, or modalities. This fragmentation limits their scalability and hinders deployment in real-world clinical environments where adaptability and generalization are essential. Despite the success of deep learning approaches, there remains a critical gap in developing unified, general-purpose diagnostic systems capable of learning across diverse imaging domains and clinical contexts. This study addresses this limitation by proposing a foundation model framework for medical imaging, leveraging large-scale multimodal pretraining to enable universal diagnostic capabilities. The approach integrates medical images, including X-ray, computed tomography, and magnetic resonance imaging, with associated clinical text to construct a shared representation space. The model is pretrained using a combination of self-supervised learning, contrastive learning, and transformer-based architectures to capture both visual and semantic relationships across modalities. The proposed framework is evaluated across multiple downstream tasks, including classification, segmentation, and detection, as well as across heterogeneous datasets representing different institutions and imaging conditions. Experimental results demonstrate that the foundation model consistently outperforms conventional convolutional and transformer-based baselines, achieving superior accuracy, robustness, and generalization. Notably, the model exhibits strong resilience to domain shift and reduced dependence on task-specific labeled data. The contributions of this work are threefold. First, it introduces a unified foundation model architecture tailored for medical imaging applications. Second, it establishes a scalable multimodal pretraining strategy that enhances cross-task and cross-domain transferability. Third, it provides comprehensive empirical validation, demonstrating the feasibility of moving toward universal diagnostic systems in clinical practice. These findings highlight the transformative potential of foundation models in redefining the future of medical imaging and intelligent healthcare systems.

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Cite this publication

Xin Nie, Xingyang Chen (2026). Foundation Models for Medical Imaging: Large-Scale Pretraining for Universal Diagnostic Systems. International Journal of Health and Medical Research, 2(01).

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