Accurately detecting pain in infants remains a complex challenge. Conventional deep neural networks used for analyzing infant cry sounds typically demand large labeled datasets, substantial computational power, and often lack interpretability. In this work, we introduce a novel approach that leverages OpenAI's vision-language model, GPT-4(V), combined with mel spectrogram-based representations of infant cries through prompting. This prompting strategy significantly reduces the dependence on large training datasets while enhancing transparency and interpretability. Using the USF-MNPAD-II dataset, our method achieves an accuracy of 83.33 % with only 16 training samples, in contrast to the $\mathbf{4, 9 1 4}$ samples required in the baseline model. To our knowledge, this represents the first application of few-shot prompting with vision-language models such as GPT-4o for infant pain classification.

Infant cries serve as a primary indicator of distress and pain; however, distinguishing pain-related cries from those triggered by other needs remains a challenging task, even for trained professionals. Timely and accurate pain assessment is essential for appropriate medical intervention, particularly in preverbal infants who cannot express their needs verbally. Recently, Deep Learning (DL) models have demonstrated significant potential in addressing this challenge by enabling automated and efficient pain assessment through audio signal processing. In this survey, we review methods for pain assessment from infant cry sounds, covering deep learning architectures, modern Transformer-based models, and emerging Vision-Language Model (VLM) pipelines. The review includes approaches that integrate Mel-spectrogram representations of cry audio with multimodal model frameworks to improve robustness, interpretability, and cross-modal reasoning in pain detection. By summarizing recent advancements and identifying limitations and open challenges in current methodologies, this review aims to provide insights into future research directions that may enhance the robustness, generalizability, and clinical applicability of automated infant pain assessment tools.