Artificial intelligence-driven precision treatment of reproductive medicine-related diseases: the optimal protocol choice for IVF-ET
This AI-assisted CDSS streamlines clinicians' decision-making by enabling efficient and accurate initial judgments on OS, standardizing and personalizing recommendations, and optimizing OS for effectiveness and cost-efficiency.
Authors
- R. Wang (ORCID: https://orcid.org/0000-0002-5059-8456)
- Shujie Liao
- Juanfang Ruan (ORCID: https://orcid.org/0000-0001-8994-5235)
- Rui Long (ORCID: https://orcid.org/0000-0002-5047-2589)
- Yichuan Zhang (ORCID: https://orcid.org/0009-0008-1142-1501)
- Chao Tian (ORCID: https://orcid.org/0000-0003-4971-770X)
- Di Wu (ORCID: https://orcid.org/0000-0002-0682-9755)
- Rongkai Chen (ORCID: https://orcid.org/0000-0003-1486-1322)
- Lei Jin (ORCID: https://orcid.org/0000-0003-3758-9640)
- Li Wen (ORCID: https://orcid.org/0000-0003-3783-3826)
- Wei Pan (ORCID: https://orcid.org/0000-0002-2166-7002)
- Cheng Hu (ORCID: https://orcid.org/0000-0003-3962-8519)
Institutions
- Wuhan University (CN)
- Zhongnan Hospital of Wuhan University (CN)
- Hubei Provincial Women and Children's Hospital (CN)
- Wuhan Children's Hospital (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Yangzhou University (CN)
- Renmin University of China (CN)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1016/j.jare.2025.10.040
- Primary Topic
- Assisted Reproductive Technology and Twin Pregnancy
- Type
- article
- Field-Weighted Citation Impact
- 0.00