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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1. 国家卫生健康委员会. 产前诊断技术管理办法. 北京: 国家卫生健康委员会, 2002.
2. 染色体微阵列分析技术在产前诊断中的应用协作组. 染色体微阵列分析技术在产前诊断中的应用专家共识. 中华妇产科杂志, 2014, 49(8): 570-572.
3. 全外显子组测序技术在产前诊断中的应用协作组. 全外显子组测序技术在产前诊断中应用的专家共识. 中华医学遗传学杂志, 2022, 39(5): 457-463.
4. 刘杏, 杨寅, 葛一平, 等. 人工智能在临床基因组学中的应用进展. 中国医学科学院学报. 2021, 43(6): 950-955.
5. 罗红, 李胜利, 邓学东, 等. 中国产前超声诊断 40 年: 技术演进、临床突破与未来发展. 中国医学影像技术, 2025, 41(8): 1333-1339.
6. 刘俊涛, 周婧文. 继往开来, 行稳致远: 我国产前筛查诊断发展历程回顾与展望. 中华围产医学杂志, 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. Özden F, Alkan C, Çiçek 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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