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

Table 3 Major limitations and challenges in the clinical integration of artificial intelligence for diagnosing gastric precancerous lesions

Category	Specific challenge/issue	Supporting evidence/explanation	Ref.
Generalizability and robustness	Performance degradation across centers	Most studies are single-center, leading to models that may not perform well on data from different hospitals, equipment, or populations	[47]
		A 2023 external validation study in Singapore found endoscopists’ average accuracy was superior to the AI system, highlighting generalizability issues	[48]
Clinical validation	Lack of high-level evidence	The majority of studies are retrospective with inherent limitations (selection bias, overfitting). Large-scale, prospective multicenter RCTs are needed	[9]
	Scarcity of RCTs in gastric cancer	A 2025 systematic review found only 11% of GI oncology AI RCTs focused on gastric cancer	[49]
	Unclear impact on patient outcomes	The feasibility, effectiveness, safety, and long-term impact on patient outcomes require validation	[34,35]
	Inconsistent conclusions on utility	Studies comparing AI against endoscopists of varying experience levels have yielded inconsistent results	[48]
Data and algorithms	High heterogeneity	High heterogeneity in algorithms, imaging techniques, and study designs makes comparing models difficult	[50]
	Lack of standardized, public datasets	Absence of public, standardized large-scale databases (e.g., “EndoNet”) introduces selection bias, limits reproducibility, and hinders fair comparison	[1,9,13]
	Suboptimal training data ratios	The positive-to-negative sample ratio in training sets influences performance, with a suggested optimal ratio between 1:1 and 1:2	[51]
	Potential for missed diagnosis	Models trained primarily on typical lesions may miss rare, atypical, or minute early lesions	[41]
Interpretability and trust	“Black box” problem	The decision-making process of many AI models lacks transparency, hindering clinician trust, acceptance, and error troubleshooting	[1]
		Although techniques like heatmaps can help, the underlying logic remains insufficiently transparent, posing an obstacle to adoption	[47,52]
Regulatory and workflow	Underdeveloped regulatory framework	Lack of unified data standards, reliable evaluation systems, and comprehensive regulatory/ethical frameworks	[13]
	Undefined human-AI collaboration	The specific role of AI (e.g., primary screening, second opinion) and how to optimize human-computer interaction require further exploration	[48]

AI: Artificial intelligence; RCT: Randomized controlled trial; GI: Gastrointestinal.