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.

Patient-derived organoids (PDOs) established via mechanical processing retain critical cellular components from the native microenvironment, including stromal elements. However, the macro-scale architecture of these organoids poses significant challenges for conventional viability assessment, often necessitating destructive processing or specialized imaging equipment. We developed a streamlined fluorescence-based protocol for rapid, population-level viability screening of large, mechanically prepared PDOs using standard widefield epifluorescence microscopy. The method employs a triple staining approach—Calcein AM, propidium iodide, and Hoechst 33342—and requires less than 1.5 hours with readily available reagents. To validate biological relevance, we performed parallel histological assessment using hematoxylin and eosin (H&E) staining. Despite geometric disparities between three-dimensional (3D) organoid fragments and two-dimensional (2D) histological sections, fluorescence-based ratiometric viability indices (Calcein/Hoechst) strongly concorded with nuclear density measurements from Day 3 to Day 8. This confirms that our volume-normalized approach accurately reflects population-level viability trends. Successfully applied to diverse models, including intrahepatic cholangiocarcinoma, glioblastoma, and abdominal aortic aneurysm organoids, the protocol demonstrates utility for culture optimization and treatment response assessment. Thus, this accessible method fills a practical niche for rapid quality control of large PDOs, distinct from single-organoid quantification which requires additional segmentation.