TY  - JOUR
AU  - Yin, Wenzhen
AU  - Feng, Yutian
AU  - Diao, Jinmei
AU  - Ma, Wenrui
AU  - Hu, Zhiyuan
AU  - Deng, Wei
AU  - Liu, Juan
PY  - 2026
TI  - Converging artificial intelligence and organoids: toward a mechanism-driven research paradigm for traditional Chinese medicine
JO  - Cell Organoid
SN  - 3007-6552
AB  - The modernization of traditional Chinese medicine (TCM) confronts a persistent bottleneck: interpreting its dynamic, holistic “formula–human body” system in the language of modern science. The multi-component, multi-target pharmacology of TCM compound formulas resists reductionist analysis, and conventional experimental models fall short of capturing the systemic regulatory effects that underlie clinical efficacy. Recent advances in artificial intelligence (AI) and organoid technology now provide complementary tools to address this long-standing challenge. AI supplies the computational power to model complex, high-dimensional biological networks; organoids furnish biologically faithful platforms to validate predictions in human-relevant contexts. Together, these technologies enable a closed-loop research paradigm—computational prediction, experimental verification, model iteration—that is high-throughput, quantifiable, and predictive. This Comment argues that the convergence of AI and organoids can drive TCM research from an experience-driven tradition toward a data-driven, mechanism-verified framework, bridging millennia of accumulated clinical wisdom with contemporary biomedical science.
UR  - https://doi.org/10.26599/CO.2026.9410023
DO  - 10.26599/CO.2026.9410023