Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions
AI has the potential to improve both personalized prediction and mechanistic understanding of RPL. However, clinical translation is currently hampered by a number of important issues, including small and diverse datasets, conflicting diagnostic definitions, limited external validation, and a lack of prospective clinical trials. To responsibly integrate AI tools into reproductive care, these limitations must be addressed.
Authors
- Ekaterini Domali (ORCID: https://orcid.org/0000-0001-8899-3040)
- Efthalia Moustakli (ORCID: https://orcid.org/0009-0005-3333-2620)
- Maria Tzeli
- Nikolaos Kathopoulis (ORCID: https://orcid.org/0000-0002-0031-809X)
- Nikolaos Thomakos (ORCID: https://orcid.org/0000-0003-0962-3549)
- Athanasios Zikopoulos (ORCID: https://orcid.org/0009-0002-9553-6803)
- Konstantinos Louis
- Ioannis Arkoulis
- Aikaterini Lydia Vogiatzoglou
- Anastasios Potiris
- Panagiotis Christopoulos
- Sofoklis Stavros
Institutions
- National and Kapodistrian University of Athens (GR)
- University of West Attica (GR)
- Alexandra Hospital (GR)
- Aretaeio Hospital (GR)
- Human Computer Interaction (Switzerland) (CH)
- Torbay and South Devon NHS Foundation Trust (GB)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2026-01-14
- DOI
- https://doi.org/10.3390/jcm15020686
- Citations
- 1
- Primary Topic
- Reproductive System and Pregnancy
- Type
- article
- Field-Weighted Citation Impact
- 24.43