Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Table 1 General features of machine learning methods in the development of artificial intelligence models in gastrointestinal pathology

AI models	Strengths	Weaknesses
ML, Traditional, Supervised	Data output can be produced from the previously labeled training set	Labeling big data takes a considerable amount of time and can be challenging
	Allows users to reflect domain knowledge features	Feature extraction quality significantly affects the accuracy
ML, Traditional, Supervised	Users do not supervise the model or label any data	Input data is unknown and not labeled
	Patterns are detected automatically	Precise information related to data sorting is not provided
	Save time	Interpretation is challenging
SVM	Suitable for more efficient regression and classification analysis with high-dimensional data	Not suitable for large data sets. Requires more time for training; Low performance in overlapping classes
CNN	No labeling is required for important information and features	Lack of interpretability due to black boxes
	The performance capacity in image recognition is high
FCN	Provides computational speed	A large amount of labeled data for training is required
	The background noise is automatically eliminated	The labeling cost is high
RNN	Able to decide which information to remember from past experiences	The model is hard to train
	A suitable deep learning model for sequential data	The computational cost is high
MIL	A detailed annotation is not required	A large amount of training data is required
	Suitable to be performed on large datasets	The computational cost is high
GAN	The potential to produce new realistic data that resembles the original data	The model is hard to train

AI: Artificial intelligence; ML: Machine learning; SVM: Support Vector Machine; CNN: Convolutional neural networks; FCN: Fully convolutional neural networks; RNN: Recurrent neural networks; MIL: Multi-instance learning; GAN: Generative adversarial networks.