InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language

Computer Science > Artificial Intelligence [Submitted on 22 Apr 2026] InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language Nicklas Neu, Thomas Ebner, Jasmin Primus, Raphael Zefferer, Bernhard Schenkenfelder, Mathias Brunbauer, Florian Kromp The application of artificial intelligence (AI) in IVF has shown promise in improving consistency and standardization of decisions, but often relies on annotated data and does not make use of the multimodal nature of IVF data. We investigated whether foundational vision-language models can be fine-tuned to predict natural language descriptions of embryo morphology and development. Using a publicly available embryo time-lapse dataset, we fine-tuned PaliGemma-2, a multi-modal vision-language model, with only 1,000 images and corresponding captions, describing embryo morphology, embryonic cell cycle and developmental stage. Our results show that the fine-tuned model, InVitroVision, outperformed a commercial model, ChatGPT 5.2, and base models in overall metrics, with performance improving with larger training datasets. This study demonstrates the potential of foundational vision-language models to generalize to IVF tasks with limited data, enabling the prediction of natural language descriptions of embryo morphology and development. This approach may facilitate the use of large language models to retrieve information and scientific evidence from relevant publications and guidelines, and has implications for few-shot adaptation to multiple downstream tasks in IVF. Comments: 15 pages, 2 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.21061 [cs.AI]   (or arXiv:2604.21061v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21061 Focus to learn more Submission history From: Florian Kromp [view email] [v1] Wed, 22 Apr 2026 20:05:37 UTC (1,535 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)