The field of medical imaging and personalized marketing is witnessing significant developments with the emergence of small language models (SLMs). These models are being explored for their potential to assist in image interpretation and clinical reasoning, as well as generate personalized marketing offers. Researchers are investigating the use of SLMs in medical imaging classification tasks, such as mammogram visual question answering and chest X-ray classification, with promising results. The use of prompt engineering and contrastive learning-based fine-tuning is shown to enhance the performance of SLMs in these tasks. Additionally, SLMs are being applied to personalized marketing, with models such as SLM4Offer and Trained Miniatures demonstrating improved offer acceptance rates and cost-effectiveness. Noteworthy papers include: Is ChatGPT-5 Ready for Mammogram VQA?, which evaluates the performance of GPT-5 in mammogram visual question answering tasks, and Applications of Small Language Models in Medical Imaging Classification with a Focus on Prompt Strategies, which investigates the use of SLMs in chest X-ray classification with different prompt strategies. SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning is also notable for its introduction of a generative AI model for personalized offer generation using contrastive learning-based fine-tuning.