Published online Jun 7, 2021. doi: 10.3748/wjg.v27.i21.2818
Peer-review started: February 4, 2021
First decision: March 6, 2021
Revised: March 16, 2021
Accepted: April 28, 2021
Article in press: April 28, 2021
Published online: June 7, 2021
Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.
Core Tip: The advances in artificial intelligence (AI) will revolutionize medical practice, as well as other areas of medicine. Deep learning algorithms have shown promising benefits in various areas of diagnostic histopathology. Despite this, AI technology is not widely used as a medical device and is not approved by a regulatory authority. Thus, implying that certain improvements in the development process are still necessary for the implementation of AI in the real-life histopathology-practice. This paper aims to provide a review of recent AI developments in gastrointestinal pathology and the challenges in their implementation.