Vision-Language Models in Medical Imaging: Transforming AI-Assisted Healthcare

Vision-language models (VLMs) are creating new opportunitiesfor medical image interpretation by connecting visualrepresentations with clinical language. Unlike conventional taskspecificmodels, these systems can support multiple applications,including disease classification, medical visual question answering,image-text retrieval, semantic segmentation, and radiology reportgeneration. However, their effectiveness depends on the qualityof paired image-text data, the adaptation strategy, the imagingmodality, and the evaluation method. This paper presents acomparative literature review of five research studies coveringconcept-based classification, multimodal pre-training, ultrasoundadaptation, and retrieval-assisted in-context learning. The reviewedapproaches demonstrate different advantages: trainingfreemodels reduce the need for retraining, physics-informedadapters improve ultrasound analysis, and joint vision-languagearchitectures support cross-modal reasoning. The literaturealso reveals limitations involving data scarcity, modality shifts,computational requirements, clinical factuality, and generalizationacross patients and institutions. The review identifies researchopportunities in three-dimensional medical imaging, clinicallygrounded report evaluation, probabilistic concept representations,and robust cross-domain adaptation. The findings suggest thatsuccessful clinical deployment requires not only improved modelperformance but also reliable validation, interpretability, andhuman oversight.