Artificial intelligence in pathological evaluation of gastrointestinal cancers

Table 4 Summary of challenges and suggested solutions in development process of artificial intelligence applications

Process	Challenges	Suggested solutions
Ethical considerations	Lack of patient’s approval for commercial use	Approval for both research and product development
Design of AI models	Underestimation of end-users’ needs	Collaboration with skate holders
Optimization of data-sets	CNN: Large amounts of images	Augmentation techniques, transfer learning
	Rare tumors: Limited number of images	Global data sharing
	Variations in preanalytical and analytical phases	AI algorithms to standardize staining, color properties, and WSIs quality
Annotation of data-sets	Interobserver variations in diagnosis	MIL algorithms
	Discrepancies among performances for trained algorithms
Validation	Presence of ground truth without objectivity	Multicenter evaluations that include many pathologists and data-set
Regulation	Lack of current regulatory guidance specific for AI tools	New guidelines and regulations for safer and effective AI tools
Implementation	Changes in work-flow	Selection of AI applications that will speed up the work-flow
	IT infrastructure investment	Augmented microscopy directed to the cloud network service
	The relative inexperience of pathologists	Training about AI, integration of AI in medical education
	AI applications that lack interpretability ( Black-box)	Constructions of interpretable models, generating attention heat map
	Lack of external quality assurance	Sheme for this purpose should be designed
	Legal implications	The performance of AI algorithms should be assured for reporting

CNN: Convolutional neural networks; MIL: Multi-instance learning.