Artificial intelligence in gastric cancer: a translational narrative review

Abstract

Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application.There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies.It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.

Scope of Artificial Intelligence in Gastrointestinal Oncology

“…Although AI was 100% accurate at differentiating benign from malignant pathology, the accuracy fell to 77% for atypia [ 137 ]. Due to the large amount of variability in reported data, AI is not yet ready for real time application in pathology practice to diagnose GI malignancies, but the ongoing work is extremely promising [ 138 ]. Overall, there is a need to identify an AI model for pathological diagnosis that is generalizable on a large scale and can be commercially developed for applicability in clinical practice.…”

Evaluation of deep learning methods for early gastric cancer detection using gastroscopic images

“…The identified early cancer and its location are displayed by applying AI technology to these archived images. AI has been used to detect gastric cancer, predict the depth of gastric cancer, and distinguish gastric cancer from gastric ulcer [ 5 , 6 , 7 , 8 ].…”