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Artif Intell Cancer. Sep 8, 2026; 7(1): 114273
Published online Sep 8, 2026. doi: 10.35713/aic.v7.i1.114273
Published online Sep 8, 2026. doi: 10.35713/aic.v7.i1.114273
Synergistic applications of artificial intelligence and organoid technology in gastric precancerous lesion research: Mechanisms, translation, and challenges
Chen-Heng Wu, Department of Digestive Endoscopy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
Jun-Xin Qiu, Yue-Bo Jia, Yi Quan, Chang Liu, Jiang-Hong Ling, Department of Gastroenterology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
Co-first authors: Chen-Heng Wu and Jun-Xin Qiu.
Co-corresponding authors: Chang Liu and Jiang-Hong Ling.
Author contributions: Wu CH and Ling JH conceived the review topic and designed the framework; Ling JH and Liu C supervised the entire work; Wu CH and Qiu JX are performed literature search, data interpretation, critical analysis, and drafted the manuscript. All authors contributed to revision and approved the final version. Wu CH and Qiu JX contributed equally to this work as co-first authors. The designation of Ling JH and Liu C as co-corresponding authors is a direct reflection of their complementary and equally critical leadership roles in the conception, execution, and synthesis of this review article. Ling JH's contribution was foundational and strategic. She provided the original intellectual vision and drive that conceived the review topic. Furthermore, she was primarily responsible for establishing the overarching intellectual framework by designing the review's structure, ensuring a coherent and logical flow of concepts. She is the key person overseeing the long-term direction of this research theme. Liu C's contribution was pivotal in translating the initial idea into a tangible, high-quality manuscript. She played a leading role in the day-to-day supervision of the literature search, data interpretation, and critical analysis processes. She provided hands-on guidance during the drafting of the manuscript and coordinated the integration of all authors' feedback during the revision phase. In summary, Ling JH served as the architect of the project's core idea and structure, while Liu C acted as the project lead who guided its detailed development and execution. Both roles were indispensable for the successful completion of this work. This dual-correspondence structure ensures that the scientific community can effectively address inquiries related to both the broad conceptual framework (directed to Ling JH) and the specific analytical methodologies and manuscript synthesis (directed to Liu C). We confirm that both authors have approved this designation.
Supported by National TCM Advantageous Specialty Project of National Administration of Traditional Chinese Medicine: State Medical Letter on Chinese Medicine, No.[2024]90; and Shuguang Hospital Siming Foundation Research Special Project, No. SGKJ-202304.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Corresponding author: Jiang-Hong Ling, MD, Department of Gastroenterology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 528 Zhang
Received: September 15, 2025
Revised: October 6, 2025
Accepted: January 12, 2026
Published online: September 8, 2026
Processing time: 351 Days and 23 Hours
Revised: October 6, 2025
Accepted: January 12, 2026
Published online: September 8, 2026
Processing time: 351 Days and 23 Hours
Core Tip
Core Tip: We propose a transformative paradigm: Using artificial intelligence to decipher dynamic organoid phenotypes for forecasting gastric cancer (GC) risk. This review details how this synergy, which integrates high-throughput organoid screening with multimodal clinical data, unlocks the “black box” of disease progression. It empowers a shift from reactive diagnosis to proactive, mechanism-based precision prevention, ultimately pioneering a new era where GC is preemptively intercepted rather than treated.