Artificial intelligence applications in upper gastrointestinal cancers

Huiyan Luo and colleagues' multicentre, case-controlled, diagnostic study 1 Luo H Xu G Li C et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019; 20: 1645-1654 Summary Full Text Full Text PDF PubMed Scopus (165) Google Scholar presented important data on the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) which, after training and validating over 1 million images via artificial intelligence, was shown to have a high diagnostic accuracy for detecting upper gastrointestinal cancers. The rapid pace of advancement in artificial intelligence has led to the development of autonomic technologies that assist doctors to make an accurate and quick decision. 2 Garcia-Vidal C Sanjuan G Puerta-Alcalde P Moreno-García E Soriano A Artificial intelligence to support clinical decision-making processes. EBioMedicine. 2019; 46: 27-29 Summary Full Text Full Text PDF PubMed Scopus (35) Google Scholar , 3 Bi WL Hosny A Schabath MB et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019; 69: 127-157 PubMed Google Scholar GRAIDS represents a tremendous opportunity to improve the accuracy of diagnosis and screening for upper gastrointestinal cancers. However, we are concerned about data interpretation and extrapolation in Luo and colleagues' study. Artificial intelligence applications in upper gastrointestinal cancers – Authors' replyWe appreciate Jialin Li and colleagues' comments on our recent study,1 which proposes use of the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for endoscopic detection of upper gastrointestinal cancers. Li and colleagues questioned whether GRAIDS is equally effective across all stages of upper gastrointestinal cancers. Unfortunately, disease staging information was not collected for the past 10 years for the large-scale retrospective images obtained from the six participating centres. Full-Text PDF Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic studyGRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses. Full-Text PDF