Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

2025-06-27

AI Summary

Medical AI for imaging faces clinical deployment bottlenecks including data scarcity, distribution shift, and limited task generalization. To address these challenges, this paper presents the first systematic survey and integration of multimodal prompting mechanisms—including textual instructions, visual prompts, and learnable embeddings—into a unified prompt engineering framework tailored for medical imaging. The framework synergistically combines prompt injection, in-context learning, and multimodal feature alignment, and is empirically evaluated across image classification, segmentation, and generation tasks. Results demonstrate substantial improvements: +3.2% average accuracy gain, 41% reduction in performance degradation under distribution shift, and enhanced decision interpretability. Critically, it achieves full-supervision baseline performance using only a small number of annotated samples. This work establishes a novel paradigm for resource-efficient, highly generalizable, and deployable medical AI systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Addressing data scarcity and distribution shifts in medical imaging
• Enhancing model adaptability without extensive retraining via prompts
• Optimizing prompt design for clinical scalability and heterogeneity

Innovation

• Prompt-based methodologies guide deep learning models
• Diverse prompt modalities enhance model adaptability
• Prompt mechanisms improve accuracy and interpretability