Artificial intelligence in inflammatory bowel disease: current status and opportunities

[7] However, all these studies were retrospective and did not involve a large sample size of IBD unclassified patients. Besides the abovementioned studies, there were a few studies using ML to construct different models to discriminate UC or CD from the healthy controls including SNPs, miRNAs, or multi-omics with accuracy ranging from 78.9% to 92.8%. Considering the AI model's generic characteristics and unavailability or transparency in calculating progress, it is always challenging to conduct external validation for the AI model. [...]it is also relatively difficult to determine why an AI model has several errors when it fails in clinical practice. [...]the AI model should develop its explainability, which is the ability to explain what happens when the model makes a decision. [...]when making decisions using the AI assistant system in future clinical practice, the healthcare providers should be well-informed or should be knowledgeable about the system.