Artificial intelligence and assisted reproductive technology: A comprehensive systematic review
The objective of this review is to evaluate the contributions of Artificial Intelligence (AI) to Assisted Reproductive Technologies (ART), focusing on its role in enhancing the processes and outcomes of fertility treatments. This study analyzed 48 relevant articles to assess the impact of AI on vari...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Taiwanese Journal of Obstetrics & Gynecology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1028455924002742 |
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| author | Yen-Chen Wu Emily Chia-Yu Su Jung-Hsiu Hou Ching-Jung Lin Krystal Baysan Lin Chi-Huang Chen |
| author_facet | Yen-Chen Wu Emily Chia-Yu Su Jung-Hsiu Hou Ching-Jung Lin Krystal Baysan Lin Chi-Huang Chen |
| author_sort | Yen-Chen Wu |
| collection | DOAJ |
| description | The objective of this review is to evaluate the contributions of Artificial Intelligence (AI) to Assisted Reproductive Technologies (ART), focusing on its role in enhancing the processes and outcomes of fertility treatments. This study analyzed 48 relevant articles to assess the impact of AI on various aspects of ART, including treatment efficacy, process optimization, and outcome prediction. The effectiveness of different machine learning paradigms—supervised, unsupervised, and reinforcement learning—in improving ART-related procedures was particularly examined. The findings indicate that AI technologies significantly enhance ART processes by refining tasks such as embryo and sperm analysis and facilitating personalized treatment plans based on predictive modeling. Notable improvements were observed in the accuracy of diagnosing and predicting successful outcomes in fertility treatments. AI-driven models provided more precise forecasts of the optimal timing for clinical interventions such as egg retrieval and embryo transfer, which are critical to the success of ART cycles. The integration of AI into ART represents a transformative advancement, substantially improving the precision and efficiency of fertility treatments. The continuous evolution of AI methodologies is likely to further revolutionize this field, enabling more tailored and successful treatment approaches. AI is becoming an indispensable tool in reproductive medicine, enhancing both the effectiveness of treatments and the clinical decision-making process. This review underscores the potential of AI to act as a catalyst for innovative solutions in the optimization of ART. |
| format | Article |
| id | doaj-art-3c7fa6336e3f40c4b251a2b1f87ea48d |
| institution | Kabale University |
| issn | 1028-4559 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Taiwanese Journal of Obstetrics & Gynecology |
| spelling | doaj-art-3c7fa6336e3f40c4b251a2b1f87ea48d2025-01-09T06:12:49ZengElsevierTaiwanese Journal of Obstetrics & Gynecology1028-45592025-01-016411126Artificial intelligence and assisted reproductive technology: A comprehensive systematic reviewYen-Chen Wu0Emily Chia-Yu Su1Jung-Hsiu Hou2Ching-Jung Lin3Krystal Baysan Lin4Chi-Huang Chen5Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, TaiwanGraduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, TaiwanDivision of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan; Graduate Institute of Medical Science, College of Medicine, Taipei Medical University, Taipei, TaiwanGraduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; Department of Obstetrics and Gynecology, Taipei Medical University Shuang Ho Hospital, Taipei, TaiwanDivision of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, TaiwanDivision of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan; Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Corresponding author. Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan.The objective of this review is to evaluate the contributions of Artificial Intelligence (AI) to Assisted Reproductive Technologies (ART), focusing on its role in enhancing the processes and outcomes of fertility treatments. This study analyzed 48 relevant articles to assess the impact of AI on various aspects of ART, including treatment efficacy, process optimization, and outcome prediction. The effectiveness of different machine learning paradigms—supervised, unsupervised, and reinforcement learning—in improving ART-related procedures was particularly examined. The findings indicate that AI technologies significantly enhance ART processes by refining tasks such as embryo and sperm analysis and facilitating personalized treatment plans based on predictive modeling. Notable improvements were observed in the accuracy of diagnosing and predicting successful outcomes in fertility treatments. AI-driven models provided more precise forecasts of the optimal timing for clinical interventions such as egg retrieval and embryo transfer, which are critical to the success of ART cycles. The integration of AI into ART represents a transformative advancement, substantially improving the precision and efficiency of fertility treatments. The continuous evolution of AI methodologies is likely to further revolutionize this field, enabling more tailored and successful treatment approaches. AI is becoming an indispensable tool in reproductive medicine, enhancing both the effectiveness of treatments and the clinical decision-making process. This review underscores the potential of AI to act as a catalyst for innovative solutions in the optimization of ART.http://www.sciencedirect.com/science/article/pii/S1028455924002742Artificial intelligenceAssisted reproductive technologyMachine learningEmbryo selectionTreatment outcome prediction |
| spellingShingle | Yen-Chen Wu Emily Chia-Yu Su Jung-Hsiu Hou Ching-Jung Lin Krystal Baysan Lin Chi-Huang Chen Artificial intelligence and assisted reproductive technology: A comprehensive systematic review Taiwanese Journal of Obstetrics & Gynecology Artificial intelligence Assisted reproductive technology Machine learning Embryo selection Treatment outcome prediction |
| title | Artificial intelligence and assisted reproductive technology: A comprehensive systematic review |
| title_full | Artificial intelligence and assisted reproductive technology: A comprehensive systematic review |
| title_fullStr | Artificial intelligence and assisted reproductive technology: A comprehensive systematic review |
| title_full_unstemmed | Artificial intelligence and assisted reproductive technology: A comprehensive systematic review |
| title_short | Artificial intelligence and assisted reproductive technology: A comprehensive systematic review |
| title_sort | artificial intelligence and assisted reproductive technology a comprehensive systematic review |
| topic | Artificial intelligence Assisted reproductive technology Machine learning Embryo selection Treatment outcome prediction |
| url | http://www.sciencedirect.com/science/article/pii/S1028455924002742 |
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