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

Table 7 Challenges and future directions for the integration of artificial intelligence and organoids in gastric precancerous lesion research

Category	Specific challenge/opportunity	Explanation/proposed Action	Expected outcome/goal	Ref.
Current challenges
Research gap	Lack of integrated studies	Almost no studies combine AI and organoid technologies. A complete lack of empirical data on their integrated application specifically to GPLs	N/A	[1]
Data dependency and validation	Limited training data	AI model performance depends on high-quality, large-scale data, yet public organoid pharmacogenomic data linked to clinical outcomes remain limited	N/A	[69]
Data standardization	Lack of unified standards	Effective integration requires standardized processes for data acquisition, processing, and annotation (imaging and organoid data). Currently, such standards are lacking	N/A	[1,13,73]
Data integration	Handling data heterogeneity	Integrating multi-omics and multi-dimensional data from different platforms/batches presents technical difficulties due to heterogeneity, sparsity, and high dimensionality	N/A	[53,76,77]
Clinical translation	Translational gap	Seamlessly integrating laboratory findings from AI and organoids into clinical workflows to improve patient outcomes remains a key challenge	N/A	[53,56,75]
Future directions
Direct integration	AI for organoid dynamic monitoring	Initiate research applying AI for real-time, label-free quantitative analysis of organoid morphological changes, proliferation, and differentiation	To high-throughput screen for factors or drugs influencing precancerous lesion progression	[38]
Prospective studies	Integrated clinical research projects	Design prospective, multicenter studies to collect imaging data and tissue samples. Build PDOs for screening/simulation, then use AI to integrate imaging and organoid data	To validate the value of synergy in risk prediction and intervention decision-making
Database construction	Multicenter standardized database	Establish a large-scale database containing clinical info, endo/pathology images, multi-omics, organoid culture/analysis, drug response, and outcome data	To provide a high-quality data foundation for developing and validating generalizable AI models	[1,69,91]
Targeted translational studies	Address specific unmet clinical needs	Use CRISPR-Cas9 in organoids to simulate mutations (e.g., abnormal folate metabolism, high RAMP1). Use AI-high-throughput platforms to screen/optimize targeted drugs	To validate efficacy for personalized treatment strategies
Algorithm development	Advanced data integration algorithms	Develop AI algorithms capable of handling data heterogeneity, imputing sparsity, and integrating multimodal data (imaging, spatial omics, scRNA-seq)	To build more comprehensive disease models and achieve better integration with organoid co-culture systems	[75,76]
Platform establishment	AI-driven HTS organoid analysis platform	Develop a dedicated AI image analysis platform for automated, quantitative dynamic monitoring of organoid growth, differentiation, and death	To enable high-throughput interpretation of drug screening results and accelerate translation from bench to bedside

AI: Artificial intelligence; HTS: High-throughput screening; PDOs: Patient-derived organoids; scRNA-seq: Single-cell RNA sequencing; GPL: Gastric precancerous lesion.