ArticleProceedings. IEEE International Symposium on Computer-Based Medical Systems2025
Few-Shot Prompting with Vision Language Model for Pain Classification in Infant Cry Sounds.
Article in Proceedings. IEEE International Symposium on Computer-Based Medical Systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
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Authors and funding
5 authors.
Funding
Abstract
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 4,914 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.
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Registered trials
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