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Here, we introduce a\u00a0zero-shot\u00a0approach leveraging\u00a0multimodal large language models (MLLMs)\u00a0to predict neighborhood health outcomes without fine-tuning or labeled data. Using physical inactivity across four major U.S. cities as a case study, we demonstrate that\u00a0task-specific prompt design\u00a0significantly enhances ChatGPT\u2019s performance, with\u00a0combination of satellite imagery\u00a0and\u00a0socioeconomic indicators\u00a0yielding optimal accuracy. City-wide implementations reveal that ChatGPT not only captures\u00a0fine-grained spatial heterogeneity\u00a0but also matches the predictive power of conventional supervised models, while circumventing the reliance on customized training sets and maintaining robustness across diverse urban contexts. By pairing\u00a0publicly available data\u00a0with general-purpose MLLMs, our framework provides policymakers and urban planners with an\u00a0efficient, transferable tool\u00a0for rapid health disparity assessment and intervention targeting. This work also underscores a paradigm shift in urban analytics\u2014from purely data-driven modeling to\u00a0knowledge-informed reasoning\u2014and expands the frontier of MLLM applications in public health.<\/jats:p>","DOI":"10.1007\/s44212-025-00092-w","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:02:27Z","timestamp":1764892947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Zero-shot prediction of neighborhood health using multimodal large language models"],"prefix":"10.1007","volume":"4","author":[{"given":"Haoxiang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Lai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"issue":"1_suppl2","key":"92_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1177\/00333549141291S206","volume":"129","author":"P Braveman","year":"2014","unstructured":"Braveman, P., & Gottlieb, L. 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