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Here, we present a generative artificial intelligence (AI) framework that directly predicts contractile behavior of human\u2010induced pluripotent stem cell\u2010derived cardiomyocytes (hiPSC\u2010CMs) from single static images. Our approach integrates a U\u2010Net\u2010based generator with a patch\u2010based generative adversarial network (GAN) discriminator to translate morphological and sarcomere structural features into pixel\u2010resolved contraction heatmaps. To further enhance performance and generalizability, we incorporated synthetic cell\u2013function pairs generated via a StyleGAN2 framework, improving prediction accuracy and perceptual similarity. Region\u2010specific and whole\u2010cell analyses revealed that AI predictions capture biologically meaningful structure\u2013function relationships, with sarcomere organization strongly associated with both contractile output and prediction fidelity. Reconstruction error emerged as an interpretable metric reflecting localized inefficiencies in sarcomere\u2010to\u2010contraction coupling. Together, this framework establishes a scalable, interpretable strategy for inferring cardiomyocyte function from static morphology, eliminating the need for time\u2010lapse imaging, and positions generative AI as a powerful tool for bridging cellular structure and function to advance high\u2010throughput phenotyping and in vitro cardiac modeling.<\/jats:p>","DOI":"10.1002\/aidi.70166","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T01:36:24Z","timestamp":1790818584000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9629-700X","authenticated-orcid":false,"given":"Andrew","family":"Kowalczewski","sequence":"first","affiliation":[{"name":"Department of Biomedical &amp; Chemical Engineering Syracuse University  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