Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

TL;DR

This survey reviews prompt engineering techniques in medical imaging, highlighting their role in improving model performance, robustness, and interpretability while discussing current challenges and future directions for clinical application.

Contribution

It provides a comprehensive analysis of various prompt modalities and their integration in medical imaging, identifying key challenges and outlining future research trajectories.

Findings

Prompt mechanisms enhance accuracy and robustness in medical imaging tasks.

Different prompt modalities improve data efficiency and model interpretability.

Persistent challenges include prompt optimization and scalability for clinical use.

Abstract

Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the need for robust task generalization. Prompt-based methodologies have emerged as a pivotal strategy to guide deep learning models, providing flexible, domain-specific adaptations that significantly enhance model performance and adaptability without extensive retraining. This systematic review critically examines the burgeoning landscape of prompt engineering in medical imaging. We dissect diverse prompt modalities, including textual instructions, visual prompts, and learnable embeddings, and analyze their integration for core tasks such as image generation, segmentation, and classification. Our synthesis reveals how these mechanisms improve task-specific outcomes by enhancing accuracy, robustness, and data efficiency and reducing reliance on manual feature engineering while fostering greater model interpretability by making the model's guidance explicit. Despite substantial advancements, we identify persistent challenges, particularly in prompt design optimization, data heterogeneity, and ensuring scalability for clinical deployment. Finally, this review outlines promising future trajectories, including advanced multimodal prompting and robust clinical integration, underscoring the critical role of prompt-driven AI in accelerating the revolution of diagnostics and personalized treatment planning in medicine.