Utilizing the Contrastive Language-Image Pre-Training Model in Chest Radiography: Current Capabilities and Practical Guidance.
Authors
Abstract
Although convolutional neural networks have significantly advanced chest radiography (CXR) analysis, they remain limited in their adaptability to diverse clinical workflows and their interpretability. Recently, contrastive language-image pre-training (CLIP) has emerged as a novel paradigm that enables flexible, zero-shot diagnostic capabilities. Among CLIP-based approaches, Xplainer is particularly noteworthy for its highly customizable prompting framework, which supports diverse queries, even for the same image. However, diagnostic performance may vary substantially depending on the prompt engineering strategy employed. This review outlines the core architecture of CLIP and explores several key CLIP-based applications developed for CXR, focusing particularly on Xplainer, an accessible online tool. We discuss practical considerations, including input image selection, effective prompt engineering strategies (e.g., optimization of prompt structure and content), and performance evaluation approaches. Ultimately, CLIP-based models such as Xplainer represent an important advancement in medical AI by providing frameworks that better align with clinical radiology workflows and support the development of clinician-centered AI systems.