Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Table 3 Advantages and disadvantages of representative machine-learning methods in the development of artificial intelligence-models for gastrointestinal pathology

AI model	Advantages	Disadvantages
Conventional ML (supervised)	User can reflect domain knowledge to features	Requires hand-crafted features; Accuracy depends heavily on the quality of feature extraction
Conventional ML (unsupervised)	Executable without labels	Results are often unstable; Interpretability of the results
Deep neural networks (CNN)	Automatic feature extraction; High accuracy	Requires a large dataset; Low explainability (Black box)
Multi-instance learning	Executable without detailed labels	Requires a large dataset; High computational cost
Semantic segmentation (FCN, U-Net)	Pixel-level detection gives the position, size, and shape of the target	High labeling cost
Recurrent neural networks	Learn sequential data	High computational cost
Generative adversarial networks	Learn to synthesize new realistic data	Complexity and instability in training

AI: Artificial intelligence; ML: Machine learning; CNN: Convolutional neural network; FCN: Fully convolutional network