AI Dataset for Embryo Selection in IVF Aims to Improve Patient Communication
A new expert-annotated dataset of embryo images with natural language descriptions has been released to support evidence-based patient communication in in-vitro fertilization. The dataset addresses limitations in current AI-assisted embryo selection methods, which often require adaptation to specific clinical data, depend on time-lapse incubators, and lack interpretability. Morphological assessment by clinical embryologists remains a crucial step in IVF, but modern patients increasingly question expert decisions, especially after unsuccessful treatments. This resource aims to provide transparent decision-making justification and foster respectful dialogue between patients and clinicians. While artificial intelligence has shown potential for automated embryo ranking and grading, its overall impact in clinical settings remains constrained. The dataset is designed to help bridge this gap by making AI reasoning more understandable. The work is documented in the arXiv preprint 2604.16528v1, which was announced as a cross-disciplinary abstract.
Key facts
- An expert-annotated dataset of embryo images with natural language descriptions has been created.
- The dataset is intended for evidence-based patient communication in in-vitro fertilization.
- Embryo selection is a crucial step in IVF, typically based on morphological assessment by clinical embryologists.
- AI methods have demonstrated potential for automated embryo ranking and grading.
- The overall impact of AI-based solutions in IVF remains limited.
- Limitations include required adaptation to custom clinical data and reliance on time-lapse incubators.
- A lack of interpretability hinders understanding of AI reasoning in embryo selection.
- Modern patients increasingly question expert decisions, especially when treatments are unsuccessful.
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- arXiv