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Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs

Karma Phuntsho, Abdullah, Kyungmi Lee, Ickjai Lee, Euijoon Ahn

Abstract

Foundation models (FMs) have demonstrated strong transferability across medical imaging tasks, yet their clinical utility depends critically on how pretrained representations are adapted to domain-specific data, supervision regimes, and deployment constraints. Prior surveys primarily emphasize architectural advances and application coverage, while the mechanisms of adaptation and their implications for robustness, calibration, and regulatory feasibility remain insufficiently structured. This review introduces a strategy-centric framework for FM adaptation in medical image analysis (MIA). We conceptualize adaptation as a post-pretraining intervention and organize existing approaches into five mechanisms: parameter-, representation-, objective-, data-centric, and architectural/sequence-level adaptation. For each mechanism, we analyze trade-offs in adaptation depth, label efficiency, domain robustness, computational cost, auditability, and regulatory burden. We synthesize evidence across classification, segmentation, and detection tasks, highlighting how adaptation strategies influence clinically relevant failure modes rather than only aggregate benchmark performance. Finally, we examine how adaptation choices interact with validation protocols, calibration stability, multi-institutional deployment, and regulatory oversight. By reframing adaptation as a process of controlled representational change under clinical constraints, this review provides practical guidance for designing FM-based systems that are robust, auditable, and compatible with clinical deployment.

Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs

Abstract

Foundation models (FMs) have demonstrated strong transferability across medical imaging tasks, yet their clinical utility depends critically on how pretrained representations are adapted to domain-specific data, supervision regimes, and deployment constraints. Prior surveys primarily emphasize architectural advances and application coverage, while the mechanisms of adaptation and their implications for robustness, calibration, and regulatory feasibility remain insufficiently structured. This review introduces a strategy-centric framework for FM adaptation in medical image analysis (MIA). We conceptualize adaptation as a post-pretraining intervention and organize existing approaches into five mechanisms: parameter-, representation-, objective-, data-centric, and architectural/sequence-level adaptation. For each mechanism, we analyze trade-offs in adaptation depth, label efficiency, domain robustness, computational cost, auditability, and regulatory burden. We synthesize evidence across classification, segmentation, and detection tasks, highlighting how adaptation strategies influence clinically relevant failure modes rather than only aggregate benchmark performance. Finally, we examine how adaptation choices interact with validation protocols, calibration stability, multi-institutional deployment, and regulatory oversight. By reframing adaptation as a process of controlled representational change under clinical constraints, this review provides practical guidance for designing FM-based systems that are robust, auditable, and compatible with clinical deployment.
Paper Structure (46 sections, 4 figures, 5 tables)

This paper contains 46 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Scope, Definitions, and Analytical Framework
  3. Definition and Scope of Adaptation
  4. Evaluation Axes for Adaptation Strategies
  5. Adaptation Strategy Matrix
  6. Taxonomy of Adaptation Strategies
  7. Parameter-Level Adaptation
  8. Full Fine-Tuning
  9. Partial Fine-Tuning
  10. Parameter-Efficient Adaptation
  11. Representation-Level Adaptation
  12. Self-Supervised Pre-Adaptation
  13. Domain-Specific SSL
  14. Feature Alignment Across Scanners and Modalities
  15. Objective-Level Adaptation
  16. ...and 31 more sections

Figures (4)

  • Figure 1: Overview of the proposed strategy-centric framework for adapting foundation models (FMs) in medical image analysis (MIA). The framework organizes adaptation as a progression from pretrained representations through five adaptation mechanisms (PLA, RLA, OLA, DCA, and ASA), evaluated across deployment-relevant axes and analyzed through task-specific failure modes.
  • Figure 2: Conceptual illustration of the relationship between adaptation depth and deployment risk in FMs for MIA. The curve schematically summarizes recurring qualitative trends reported across the literature. The figure is not derived from quantitative modeling and serves as analytical intuition for the proposed framework.
  • Figure 3: Mechanism-level taxonomy of FM adaptation in MIA.
  • Figure 4: Conceptual spectrum of parameter-level adaptation strategies. Methods differ in representational plasticity, defined by the proportion of pretrained parameters updated during adaptation. Strategies toward the right enable deeper domain realignment but increase the risk of representational drift and validation burden.