mlsanityData to 14 Oct 2026

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

arXiv 2604.21061 ↗First posted 22 Apr 2026cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 5 of its 22 references that are in the map. First posted on arXiv 22 Apr 2026.

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5 of 22 references.

References · 5

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  3. 2024PaliGemma 2: A Family of Versatile VLMs for Transfercited by 207
  4. 2021CIDEr-R: Robust Consensus-based Image Description Evaluationcited by 22
  5. 2026Expert-Annotated Embryo Image Dataset with Natural Language Descriptions for Evidence-Based Patient Communication in IVFcited by 1

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  2. 2025Generating Synthetic Human Blastocyst Images for In-Vitro Fertilization Blastocyst Grading
  3. 2025Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
  4. 2025FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound
  5. 2025The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality
  6. 2026Scientific Domain Knowledge Improves Vision-Language Fundus Models
  7. 2026Aloe-Vision: Robust Vision-Language Models for Healthcare
  8. 2025Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning