Opportunities and challenges of prenatal diagnosis in the era of artificial intelligence_West China Medical Journal

Authors：

HU Ting ^1,2 , LEI Yinjie ³ , LIU Xijing ^1,2 , XIAO Xiao ^1,2 , SUN Yuanyuan ^1,2 , GUO Xinpeng ^1,4 , WANG Zixuan ³ ,  LIU Shanling ^1,2

1. Department of Medical Genetics / Prenatal Diagnosis Center, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan 610041, P. R. China;
3. College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610064, P. R. China;
4. West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

LIU Shanling, Email: sunny630@126.com

Keywords：

Prenatal diagnosis; artificial intelligence; clinical application

DOI：

10.7507/1002-0179.202512042

Video：

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Prenatal diagnosis, as one of the core components in the prevention and control of birth defects, is constrained by both “time sensitivity” and “data availability”. The diagnostic model driven by expert experience and manual interpretation can no longer meet the demands of rapidly evolving detection technologies, which generate massive, high-dimensional data. Additionally, issues such as delayed professional training and regional development imbalances further hinder the overall improvement of prenatal diagnosis efficacy. This article systematically elaborates on typical application scenarios of artificial intelligence-assisted prenatal diagnosis from two core aspects: the intelligent optimization of diagnostic technologies and the standardization of institutional and personnel management. It also explores the potential of emerging intelligent technologies like federated learning and digital twins, aiming to promote the transformation and upgrading of the prenatal diagnosis field from standardization and normalization toward precision and systematic high-quality development.

Citation： HU Ting, LEI Yinjie, LIU Xijing, XIAO Xiao, SUN Yuanyuan, GUO Xinpeng, WANG Zixuan, LIU Shanling. Opportunities and challenges of prenatal diagnosis in the era of artificial intelligence. West China Medical Journal, 2026, 41(1): 6-11. doi: 10.7507/1002-0179.202512042 Copy

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3.	全外顯子組測序技術在產前診斷中的應用協作組. 全外顯子組測序技術在產前診斷中應用的專家共識. 中華醫學遺傳學雜志, 2022, 39(5): 457-463.
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1. 國家衛生健康委員會. 產前診斷技術管理辦法. 北京: 國家衛生健康委員會, 2002.
2. 染色體微陣列分析技術在產前診斷中的應用協作組. 染色體微陣列分析技術在產前診斷中的應用專家共識. 中華婦產科雜志, 2014, 49(8): 570-572.
3. 全外顯子組測序技術在產前診斷中的應用協作組. 全外顯子組測序技術在產前診斷中應用的專家共識. 中華醫學遺傳學雜志, 2022, 39(5): 457-463.
4. 劉杏, 楊寅, 葛一平, 等. 人工智能在臨床基因組學中的應用進展. 中國醫學科學院學報. 2021, 43(6): 950-955.
5. 羅紅, 李勝利, 鄧學東, 等. 中國產前超聲診斷 40 年: 技術演進、臨床突破與未來發展. 中國醫學影像技術, 2025, 41(8): 1333-1339.
6. 劉俊濤, 周婧文. 繼往開來, 行穩致遠: 我國產前篩查診斷發展歷程回顧與展望. 中華圍產醫學雜志, 2023, 26(11): 919-924.
7. Zhu Y, Chau MHK, Wang H, et al. Clinical validation of artificial intelligence-assisted karyotyping on peripheral blood in a cytogenetic diagnostic laboratory. Hum Genet, 2025, 144(11/12): 1269-1276.
8. Yang C, Li T, Dong Q, et al. Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes. Med Eng Phys, 2023, 121: 104064.
9. Vylala A, Plakkottu Radhakrishnan B, Balakrishnan Kadan A. Early prediction and risk analysis using hybrid deep learning techniques in multimodal biomedical image. Dev Neurobiol, 2025, 85(4): e23001.
10. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, et al. Predicting splicing from primary sequence with deep learning. Cell, 2019, 176(3): 535-548.e24.
11. Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet, 2016, 99(4): 877-885.
12. Pounraja VK, Jayakar G, Jensen M, et al. A machine-learning approach for accurate detection of copy number variants from exome sequencing. Genome Res, 2019, 29(7): 1134-1143.
13. ?zden F, Alkan C, ?i?ek AE. Polishing copy number variant calls on exome sequencing data via deep learning. Genome Res, 2022, 32(6): 1170-1182.
14. Tan R, Shen Y. Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning. Nucleic Acids Res, 2022, 50(21): e123.
15. Melidis DP, Landgraf C, Schmidt G, et al. GenOtoScope: towards automating ACMG classification of variants associated with congenital hearing loss. PLoS Comput Biol, 2022, 18(9): e1009785.
16. Li W, Li X, Lavallee E, et al. From text to translation: using language models to prioritize variants for clinical review. medRxiv, 2025: 2024.12. 31.24319792.
17. Yang J, Liu C, Deng W, et al. Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT. Patterns (N Y), 2023, 5(1): 100887.
18. Dias R, Ali Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med, 2019, 11(1): 70.
19. De La Vega FM, Chowdhury S, Moore B, et al. Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases. Genome Med, 2021, 13(1): 153.
20. Ménard T. Good quality practices for artificial intelligence in genetics. Eur J Hum Genet, 2022, 30(9): 993-995.
21. Youssef A, Pencina M, Thakur A, et al. External validation of AI models in health should be replaced with recurring local validation. Nat Med, 2023, 29(11): 2686-2687.
22. Walton NA, Nagarajan R, Wang C, et al. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc, 2024, 31(2): 536-541.
23. Diniz PHB, Yin Y, Collins S. Deep learning strategies for ultrasound in pregnancy. Eur Med J Reprod Health, 2020, 6(1): 73-80.
24. Feng J, Kim YK, Liu P. Image shadow detection and removal based on region matching of intelligent computing. Comput Intell Neurosci, 2022, 2022: 7261551.
25. Codaccioni C, Arthuis C, Deloison B, et al. Offline ultrasound-MRI fusion imaging for assessment of normal fetal brain development. Ultrasound Obstet Gynecol, 2025, 66(4): 509-517.
26. Lei T, Zheng Q, Feng J, et al. Enhancing trainee performance in obstetric ultrasound through an artificial intelligence system: randomized controlled trial. Ultrasound Obstet Gynecol, 2024, 64(4): 453-462.
27. Mao D, Liu C, Wang L, et al. AI-MARRVEL - a knowledge-driven AI system for diagnosing mendelian disorders. NEJM AI, 2024, 1(5): 10.1056/aioa2300009.
28. Liang L, Chen Y, Wang T, et al. Genetic transformer: an innovative large language model driven approach for rapid and accurate identification of causative variants in rare genetic diseases. medRxiv, 2024: 2024.07. 18.24310666.
29. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med, 2020, 3: 119.
30. Teo ZL, Jin L, Li S, et al. Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture. Cell Rep Med, 2024, 5(2): 101419.
31. Chappell J, Aughwane R, Clark AR, et al. A review of feto-placental vasculature flow modelling. Placenta, 2023, 142: 56-63.

West China Medical Journal

Opportunities and challenges of prenatal diagnosis in the era of artificial intelligence

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