Synergistic applications of artificial intelligence and organoid technology in gastric precancerous lesion research: Mechanisms, translation, and challenges

Table 6 Proposed future research directions for organoid models in gastric precancerous lesion research

Research direction	Specific goals/actions	Expected outcomes/rationale	Ref.
Model development	Establish precancerous lesion organoid biobanks	Develop organoid models from patient tissues (e.g., with IM) and validate their ability to simulate malignant transformation in vitro, enabling study of key molecular events and driver genes	[17]
Enhanced complexity (co-culture)	Develop complex co-culture systems	Create co-culture organoid or “organoid-on-a-chip” systems incorporating vascular networks, immune cells (T cells, macrophages), stromal cells (fibroblasts), and H. pylori	[54,64,65]
		Utilize microfluidic technology for “tumor-on-a-chip” models to accurately mimic the complex TME for studying immune escape and prevention
		Employ 3D bioprinting to construct precise TMEs that better simulate in vivo responses to drugs, particularly immunotherapies
Standardization and biobanking	Establish SOPs and quality control systems	Promote the development of biobanks with detailed clinical/pathological information. Formulate standardized SOPs for culture, qualification, functional analysis, and data handling to enhance consistency and comparability	[54,55,68]
High-throughput screening (HTS)	Develop automated HTS platforms	Enhance the efficiency of organoid culture and drug testing through automation, microfluidics, acoustic manipulation, and high-content imaging	[70-72]
	Reduce costs	Explore low-cost alternative materials (e.g., synthetic hydrogels) to replace Matrigel, reducing the economic burden for large-scale screening and promoting translation	[70,72]
Comparative studies	Conduct multi-model comparison studies	Directly compare organoids vs GEMMs, chemical animal models, and ALI models in simulating the “Correa cascade” to determine the best model for specific research objectives and clarify organoid applications	[6,67]
Predictive diagnostics	Explore application in risk stratification	Use organoids from patients with different risk grades (e.g., LGIN vs HGIN) integrated with scRNA-seq to identify molecular features predictive of progression, enabling personalized risk assessment

ALI: Air-liquid interface; GEMM: Genetically engineered mouse model; HGIN: High-grade intraepithelial neoplasia; IM: Intestinal metaplasia; LGIN: Low-grade intraepithelial neoplasia; scRNA-seq: Single-cell RNA sequencing; SOP: Standard operating procedure; TME: Tumor microenvironment; H. pylori: Helicobacter pylori.