Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

Abstract

ObjectivesArtificial intelligence (AI) uses deep learning functionalities that may enhance the detection of early gastric cancer during endoscopy. An AI‐based endoscopic system for upper endoscopy was recently developed in Japan. We aim to validate this AI‐based system in a Singaporean cohort.MethodsThere were 300 de‐identified still images prepared from endoscopy video files obtained from subjects that underwent gastroscopy in National University Hospital (NUH). Five specialists and 6 non‐specialists (trainees) from NUH were assigned to read and categorize the images into “neoplastic” or “non‐neoplastic.” Results were then compared with the readings performed by the endoscopic AI system.ResultsThe mean accuracy, sensitivity, and specificity for the 11 endoscopists were 0.847, 0.525, and 0.872, respectively. These values for the AI‐based system were 0.777, 0.591, and 0.791, respectively. While AI in general did not perform better than endoscopists on the whole, in the subgroup of high‐grade dysplastic lesions, only 29.1% were picked up by the endoscopist rating, but 80% were classified as neoplastic by AI (P = 0.0011). The average diagnostic time was also faster in AI compared with endoscopists (677.1 s vs 42.02 s (P < 0.001).ConclusionWe demonstrated that an AI system developed in another health system was comparable in diagnostic accuracy in the evaluation of static images. AI systems are faster and not fatigable and may have a role in augmenting human diagnosis during endoscopy. With more advances in AI and larger studies to support its efficacy it would likely play a larger role in screening endoscopy in future.

AI and professional liability assessment in healthcare. A revolution in legal medicine?

“…( 3 ) Artificial intelligence systems that have not been adequately trained by their developers or are affected by biases.…”

“…Artificial intelligence (AI) has multiple applications in the medical-surgical context, both in scientific research and clinical care. Artificial intelligence has been and is used, for example, for the following purposes: (1) the diagnostic interpretation of images in ophthalmology ( 1 ), dermatology ( 2 ), gastroenterology ( 3 ), anatomic pathology ( 4 ), and radiology ( 5 ); (2) the interpretation of signals derived from electronic devices ( 6 ) and molecular data (genetics, tumor markers, protein structures, and medical records with medical history collection) ( 7 ); (3) the development of vaccines and drugs ( 8 ); (4) the prediction of access volume for healthcare facilities, the risk of complications in hospitalized patients, potential hospitalization, potentially serious complications, and prognosis ( 9 – 11 ); (5) the more precise classification of diseases ( 12 ); and (6) robotic surgery ( 13 , 14 ).…”