Volume 9, Issue 4 (2025)
SJMR 2025, 9(4): 231-237 |
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Shafti V, Azarboo A. Personalized Medicine and Artificial Intelligence in Ovarian Stimulation Protocols for Female Infertility: A Review Article. SJMR 2025; 9 (4) : 7
URL: http://saremjrm.com/article-1-352-en.html
URL: http://saremjrm.com/article-1-352-en.html
1- School of Medicine, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran
2- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
2- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (294 Views)
Introduction: In vitro fertilization (IVF) assisted by artificial intelligence (AI) is a rapidly evolving research field. This approach enhances ovarian stimulation outcomes and efficiency, personalizes drug dosage and timing, and streamlines the IVF process, ultimately leading to improved clinical outcomes.
Materials and Methods: This study was conducted as a systematic review. Relevant literature was retrieved from reputable scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar. Articles published between 2010 and 2024 on personalized medicine, AI, and ovarian stimulation protocols in female infertility were reviewed. The selected keywords included "artificial intelligence," "personalized medicine," "ovarian stimulation," "female infertility," "IVF treatment," "clinical decision-making," and "decision support systems." Studies were screened based on predefined inclusion and exclusion criteria, encompassing original research articles, clinical trials, systematic reviews, and meta-analyses. Data from the selected studies were extracted and analyzed to identify current trends, potential applications, advantages, challenges, and future directions for integrating AI into ovarian stimulation protocols. Furthermore, the role of advanced technologies such as machine learning (ML), artificial neural networks (ANN), and clinical decision support systems (CDSS) in optimizing the IVF process was examined.
Conclusion: This review aimed to provide an overview of the role of personalized medicine and AI in ovarian stimulation protocols for infertility treatment. This innovative approach not only plays a crucial role in IVF clinics but also enhances treatment outcomes and reduces pregnancy-related complications.
Materials and Methods: This study was conducted as a systematic review. Relevant literature was retrieved from reputable scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar. Articles published between 2010 and 2024 on personalized medicine, AI, and ovarian stimulation protocols in female infertility were reviewed. The selected keywords included "artificial intelligence," "personalized medicine," "ovarian stimulation," "female infertility," "IVF treatment," "clinical decision-making," and "decision support systems." Studies were screened based on predefined inclusion and exclusion criteria, encompassing original research articles, clinical trials, systematic reviews, and meta-analyses. Data from the selected studies were extracted and analyzed to identify current trends, potential applications, advantages, challenges, and future directions for integrating AI into ovarian stimulation protocols. Furthermore, the role of advanced technologies such as machine learning (ML), artificial neural networks (ANN), and clinical decision support systems (CDSS) in optimizing the IVF process was examined.
Conclusion: This review aimed to provide an overview of the role of personalized medicine and AI in ovarian stimulation protocols for infertility treatment. This innovative approach not only plays a crucial role in IVF clinics but also enhances treatment outcomes and reduces pregnancy-related complications.
Article number: 7
Keywords: Artificial Intelligence, Personalized Medicine, Decision Support Systems, Assisted Reproductive Technology (ART), Infertility
Article Type: Systematical Review |
Subject:
Sterility
Received: 2025/01/14 | Accepted: 2025/02/8 | Published: 2025/03/16
Received: 2025/01/14 | Accepted: 2025/02/8 | Published: 2025/03/16
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