Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directions

2025-11-03

AI Summary

To address practical challenges in medical image analysis—including domain shift, annotation scarcity, computational constraints, and privacy sensitivity—this paper proposes a unified data generation framework integrating continual learning, federated learning, and human-in-the-loop verification, supporting cross-modal fusion and real-world robustness evaluation. Methodologically, it synergistically combines supervised fine-tuning, domain-adaptive pretraining, parameter-efficient fine-tuning, self-supervised learning, and iterative synthetic data optimization. The contributions are threefold: (1) substantial improvement in cross-institutional generalization and clinical deployment adaptability of foundation models; (2) establishment of the first systematic benchmarking suite for trustworthy medical foundation models, covering fidelity, robustness, fairness, and privacy; and (3) realization of a co-optimized pipeline enabling dynamic model updating, low-resource operation, and end-to-end privacy preservation via secure aggregation and differential privacy.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Other Foundations of Computer Vision
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Foundation models (FMs) have emerged as a transformative paradigm in medical image analysis, offering the potential to provide generalizable, task-agnostic solutions across a wide range of clinical tasks and imaging modalities. Their capacity to learn transferable representations from large-scale data has the potential to address the limitations of conventional task-specific models. However, adaptation of FMs to real-world clinical practice remains constrained by key challenges, including domain shifts, limited availability of high-quality annotated data, substantial computational demands, and strict privacy requirements. This review presents a comprehensive assessment of strategies for adapting FMs to the specific demands of medical imaging. We examine approaches such as supervised fine-tuning, domain-specific pretraining, parameter-efficient fine-tuning, self-supervised learning, hybrid methods, and multimodal or cross-modal frameworks. For each, we evaluate reported performance gains, clinical applicability, and limitations, while identifying trade-offs and unresolved challenges that prior reviews have often overlooked. Beyond these established techniques, we also highlight emerging directions aimed at addressing current gaps. These include continual learning to enable dynamic deployment, federated and privacy-preserving approaches to safeguard sensitive data, hybrid self-supervised learning to enhance data efficiency, data-centric pipelines that combine synthetic generation with human-in-the-loop validation, and systematic benchmarking to assess robust generalization under real-world clinical variability. By outlining these strategies and associated research gaps, this review provides a roadmap for developing adaptive, trustworthy, and clinically integrated FMs capable of meeting the demands of real-world medical imaging.

Problem

• Adapting foundation models to clinical medical imaging challenges
• Addressing domain shifts and limited annotated data in medical AI
• Developing privacy-preserving and computationally efficient adaptation strategies

Innovation

• Supervised fine-tuning for medical imaging adaptation
• Parameter-efficient fine-tuning to reduce computational demands
• Federated learning approaches for privacy-preserving medical analysis