Artificial intelligence-driven precision diagnosis and treatment for rare diseases: practices and explorations_West China Medical Journal

Authors：

GONG Li ^1,2,3 , XIANG Yang ^1,2 , BU Jiabin ^1,2 , CHENG Nansheng ^1,4 ,  WAN Zhi ^1,5

1. Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Outpatient Department, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
3. School of Public Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, P. R. China;
4. Division of Biliary Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
5. Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

WAN Zhi, Email: 303680215@qq.com

Keywords：

Artificial intelligence; rare diseases; auxiliary diagnosis; treatment decision-making; multi-modal data

DOI：

10.7507/1002-0179.202512101

Video：

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Abstract Full text Figures/Tables Video References Cited by

Rare diseases are characterized by low incidence rates, high heterogeneity, and significant genetic relevance, posing global challenges in clinical diagnosis and treatment, including delayed diagnosis and a scarcity of therapeutic options. Artificial intelligence (AI) technology offers novel solutions to address these challenges in the field of rare diseases. This paper explores the advancements in AI applications for rare diseases from two perspectives: auxiliary diagnosis and treatment decision-making. In terms of auxiliary diagnosis, AI can integrate superficial features, electronic health records, genomic data, and multi-modal data to achieve early and precise diagnosis. Regarding treatment decision-making, AI facilitates drug target discovery, drug repurposing, and the design of gene therapy vectors, thereby promoting the development and application of new treatments. Furthermore, this paper analyzes the challenges of AI in rare disease diagnosis and treatment concerning data, technical algorithms, and clinical application, and proposes future directions, including the construction of a collaborative data ecosystem, enhancement of algorithm interpretability, and improvement of regulatory frameworks.

Citation： GONG Li, XIANG Yang, BU Jiabin, CHENG Nansheng, WAN Zhi. Artificial intelligence-driven precision diagnosis and treatment for rare diseases: practices and explorations. West China Medical Journal, 2026, 41(1): 1-5. doi: 10.7507/1002-0179.202512101 Copy

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2.	The Lancet Diabetes Endocrinology. Spotlight on rare diseases. Lancet Diabetes Endocrinol, 2019, 7(2): 75.
3.	Ferreira CR. The burden of rare diseases. Am J Med Genet A, 2019, 179(6): 885-892.
4.	張抒揚, 董咚. 2020 中國罕見病綜合社會調研. 北京: 人民衛生出版社, 2020.
5.	張抒揚. 2022 年中國罕見病臨床診療現狀調研報告. 北京: 人民衛生出版社, 2023.
6.	Schaefer J, Lehne M, Schepers J, et al. The use of machine learning in rare diseases: a scoping review. Orphanet J Rare Dis, 2020, 15(1): 145.
7.	Song J, He M, Zheng X, et al. Face-based machine learning diagnostics: applications, challenges and opportunities. Artif Intell Rev, 2025, 58(8): 1-71.
8.	Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
9.	Yamada Y, Kobayashi M, Shinkawa K, et al. Utility of synthetic musculoskeletal gaits for generalizable healthcare applications. Nat Commun, 2025, 16(1): 6188.
10.	Jee J, Fong C, Pichotta K, et al. Automated real-world data integration improves cancer outcome prediction. Nature, 2024, 636(8043): 728-736.
11.	龔力, 周永召, 何謙, 等. 互聯網+數智賦能: 罕見病全程管理的華西創新與實踐. 華西醫學, 2025, 40(8): 1309-1312.
12.	Michalski AA, Lis K, Stankiewicz J, et al. Supporting the diagnosis of fabry disease using a natural language processing-based approach. J Clin Med, 2023, 12(10): 3599.
13.	Supernat A, Vidarsson OV, Steen VM, et al. Comparison of three variant callers for human whole genome sequencing. Sci Rep, 2018, 8(1): 17851.
14.	Cheng J, Novati G, Pan J, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science, 2023, 381(6664): eadg7492.
15.	王煜坤, 劉潔. 人工智能助力皮膚罕見病診療. 罕見病研究, 2023, 2(2): 157-163.
16.	Zhao W, Wu C, Fan Y, et al. An agentic system for rare disease diagnosis with traceable reasoning. ArXiv, 2025: 2506.20430v1cs.
17.	Chirmule N, Feng H, Cyril E, et al. Orphan drug development: challenges, regulation, and success stories. J Biosci, 2024, 49: 30.
18.	弓孟春, 焦塬石, 馬武仁, 等. 人工智能支持罕見病診療的研究進展. 罕見病研究, 2022, 1(2): 101-109.
19.	Xu Z, Ren F, Wang P, et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat Med, 2025, 31(8): 2602-2610.
20.	Huang K, Chandak P, Wang Q, et al. A foundation model for clinician-centered drug repurposing. Nat Med, 2024, 30(12): 3601-3613.
21.	任建偉, 鄭昕, 韓曉紅. 腺相關病毒載體基因治療藥物生物分析技術及藥代/藥效動力學研究進展. 罕見病研究, 2024, 3(3): 350-357.
22.	Eid FE, Chen AT, Chan KY, et al. Systematic multi-trait AAV capsid engineering for efficient gene delivery. Nat Commun, 2024, 15(1): 6602.
23.	Song C, Wang Q, Zhu P, et al. Efficacy and preliminary safety assessment of EXG001-307 AAV gene therapy for spinal muscular atrophy. Mol Ther Methods Clin Dev, 2025, 33(2): 101475.
24.	Raycheva R, Kostadinov K, Mitova E, et al. Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review. Front Public Health, 2023, 11: 1214766.
25.	Gostin LO, Halabi SF, Wilson K. Health data and privacy in the digital era. JAMA, 2018, 320(3): 233-234.
26.	Shaw J, Rudzicz F, Jamieson T, et al. Artificial intelligence and the implementation challenge. J Med Internet Res, 2019, 21(7): e13659.
27.	何麗. 人工智能輔助醫療決策的歸責難題新解. 自然辯證法研究, 2023, 39(6): 65-71.

1. Cyske Z, Radzanowska-Alenowicz E, Rintz E, et al. The rare disease burden: a multidimensional challenge. Acta Biochim Pol, 2025, 72: 14777.
2. The Lancet Diabetes Endocrinology. Spotlight on rare diseases. Lancet Diabetes Endocrinol, 2019, 7(2): 75.
3. Ferreira CR. The burden of rare diseases. Am J Med Genet A, 2019, 179(6): 885-892.
4. 張抒揚, 董咚. 2020 中國罕見病綜合社會調研. 北京: 人民衛生出版社, 2020.
5. 張抒揚. 2022 年中國罕見病臨床診療現狀調研報告. 北京: 人民衛生出版社, 2023.
6. Schaefer J, Lehne M, Schepers J, et al. The use of machine learning in rare diseases: a scoping review. Orphanet J Rare Dis, 2020, 15(1): 145.
7. Song J, He M, Zheng X, et al. Face-based machine learning diagnostics: applications, challenges and opportunities. Artif Intell Rev, 2025, 58(8): 1-71.
8. Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
9. Yamada Y, Kobayashi M, Shinkawa K, et al. Utility of synthetic musculoskeletal gaits for generalizable healthcare applications. Nat Commun, 2025, 16(1): 6188.
10. Jee J, Fong C, Pichotta K, et al. Automated real-world data integration improves cancer outcome prediction. Nature, 2024, 636(8043): 728-736.
11. 龔力, 周永召, 何謙, 等. 互聯網+數智賦能: 罕見病全程管理的華西創新與實踐. 華西醫學, 2025, 40(8): 1309-1312.
12. Michalski AA, Lis K, Stankiewicz J, et al. Supporting the diagnosis of fabry disease using a natural language processing-based approach. J Clin Med, 2023, 12(10): 3599.
13. Supernat A, Vidarsson OV, Steen VM, et al. Comparison of three variant callers for human whole genome sequencing. Sci Rep, 2018, 8(1): 17851.
14. Cheng J, Novati G, Pan J, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science, 2023, 381(6664): eadg7492.
15. 王煜坤, 劉潔. 人工智能助力皮膚罕見病診療. 罕見病研究, 2023, 2(2): 157-163.
16. Zhao W, Wu C, Fan Y, et al. An agentic system for rare disease diagnosis with traceable reasoning. ArXiv, 2025: 2506.20430v1cs.
17. Chirmule N, Feng H, Cyril E, et al. Orphan drug development: challenges, regulation, and success stories. J Biosci, 2024, 49: 30.
18. 弓孟春, 焦塬石, 馬武仁, 等. 人工智能支持罕見病診療的研究進展. 罕見病研究, 2022, 1(2): 101-109.
19. Xu Z, Ren F, Wang P, et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat Med, 2025, 31(8): 2602-2610.
20. Huang K, Chandak P, Wang Q, et al. A foundation model for clinician-centered drug repurposing. Nat Med, 2024, 30(12): 3601-3613.
21. 任建偉, 鄭昕, 韓曉紅. 腺相關病毒載體基因治療藥物生物分析技術及藥代/藥效動力學研究進展. 罕見病研究, 2024, 3(3): 350-357.
22. Eid FE, Chen AT, Chan KY, et al. Systematic multi-trait AAV capsid engineering for efficient gene delivery. Nat Commun, 2024, 15(1): 6602.
23. Song C, Wang Q, Zhu P, et al. Efficacy and preliminary safety assessment of EXG001-307 AAV gene therapy for spinal muscular atrophy. Mol Ther Methods Clin Dev, 2025, 33(2): 101475.
24. Raycheva R, Kostadinov K, Mitova E, et al. Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review. Front Public Health, 2023, 11: 1214766.
25. Gostin LO, Halabi SF, Wilson K. Health data and privacy in the digital era. JAMA, 2018, 320(3): 233-234.
26. Shaw J, Rudzicz F, Jamieson T, et al. Artificial intelligence and the implementation challenge. J Med Internet Res, 2019, 21(7): e13659.
27. 何麗. 人工智能輔助醫療決策的歸責難題新解. 自然辯證法研究, 2023, 39(6): 65-71.

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