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

Table 4 Proposed future research directions for artificial intelligence in diagnosing gastric precancerous lesions

Research direction	Specific goals/actions	Expected outcomes/rationale	Ref.
High-quality clinical trials	Conduct large-scale, multicenter, prospective RCTs	Evaluate real-world effectiveness, safety, generalizability, cost-effectiveness, and impact on patient outcomes to provide high-level evidence for clinical adoption	[34,49]
	Validate clinical utility in risk stratification and prediction	Bridge the gap between basic research and clinical application, strengthening translational research	[53]
Data standardization and infrastructure	Create public, standardized, large-scale image databases	Contain diverse images from different regions, equipment, and pathologies. Enhance research transparency, reproducibility, and facilitate fair algorithm comparison and improvement	[1]
	Develop international, multicenter, annotated databases (e.g., “EndoNet”)	Make high-quality data accessible to the research community to overcome a major current limitation
	Establish guidelines for data acquisition and model validation	Improve the reproducibility and reliability of AI research through academia-industry collaboration	[53,56]
Targeted algorithm development	Enhance detection of atypical/minute lesions	Employ augmented datasets enriched with rare cases for training to reduce miss rates and improve robustness
	Establish rigorous evaluation frameworks and ethical guidelines	Ensure model safety, reproducibility, and validate clinical translation potential	[54,55]
Comparative and optimization studies	Head-to-head comparison of AI algorithms and imaging techniques	Identify the optimal AI technical solution for specific clinical scenarios (e.g., screening vs depth assessment)	[20]
	Explore optimal human-AI collaboration models	Compare AI performance against endoscopists of varying experience; explore modes like real-time assistance, second reader, or quality control monitor to clarify AI’s best clinical role	[48]
	Optimize human-computer interaction interface and workflow	Maximize diagnostic efficiency and accuracy in clinical practice
Explainable AI	Develop interpretable AI models using heatmaps, attention mechanisms, etc.	Visualize the diagnostic basis of models, make the decision-making process transparent to clinicians, and enhance trust and efficiency in human-AI collaboration	[43,47,57]
Multimodal data fusion	Integrate endoscopic images, pathology, genomics, and clinical data	Build more comprehensive, robust, and accurate fused AI models for diagnosis, risk stratification, and prognosis prediction, enabling personalized medicine	[14,47]

AI: Artificial intelligence; RCT: Randomized controlled trial.