Multimodal artificial intelligence integrates imaging, endoscopic, and omics data for intelligent decision-making in individualized gastrointestinal tumor treatment

Table 5 Translation roadmap for clinical application of reinforcement learning and self-supervised learning in gastrointestinal tumors

Phase	Timeframe	Core objective	Key technical milestones	Clinical & regulatory milestones
Short-term	1-3 years	Foundational development & algorithmic validation	(1) Complete SSL model pre-training using large-scale historical data; (2) Construct RL simulation environments based on historical outcomes; and (3) Validate superior predictive accuracy of integrated models vs baselines on retrospective data	(1) Publication of proof-of-concept studies; and (2) Establishment of open-source benchmark datasets and simulation platforms
Mid-term	3-5 years	Clinical trials in limited settings & system integration	(1) Develop interpretable, human-in-the-loop CDSS; (2) Model outputs serve as assistive decision aids) for clinicians; and (3) Validate system usability and clinician acceptance in prospective observational studies	(1) Obtain initial regulatory approval (e.g., as Class II medical device software); and (2) Develop clinical workflow integration guidelines
Long-term	5+ years	Widespread integration & adaptive learning systems	(1) Achieve multi-center deployment using privacy-preserving techniques (e.g., Federated Learning); (2) Explore regulated continuous learning and model adaptation; and (3) Conduct large-scale RCTs with OS as a primary endpoint	(1) Confirm clinical benefit through high-level evidence; (2) Establish new standards for individualized care; and (3) Advocate for healthcare reimbursement policy coverage

SSL: Self-supervised learning; RL: Reinforcement learning; CDSS: Clinical decision support systems; RCT: Randomized controlled trial; OS: Overall survival.