Artificial intelligence in gastroenterology: where are we heading?
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COMMENTARY
Artificial intelligence in gastroenterology: where are we heading? Joseph JY Sung (
✉)1, Nicholas CH Poon2
1 Institute of Digestive Disease, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; 2Big Data Decision Analytic Cancer, The Chinese University of Hong Kong, Hong Kong, China
© Higher Education Press 2020
Abstract Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancements in scientific literature, suggesting appropriate therapies, to predicting prognosis and outcome of diseases and conditions, AI is offering unprecedented possibilities to improve care for patients. Gastroenterology is a field that AI can make a significant impact. This is partly because the diagnosis of gastrointestinal conditions relies a lot on image-based investigations and procedures (endoscopy and radiology). AI-assisted image analysis can make accurate assessment and provide more information than conventional analysis. AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments. In managing relapsing and remitting diseases such as inflammatory bowel disease, irritable bowel syndrome, and peptic ulcer bleeding, convoluted neural network may formulate models to predict disease outcome, enhancing treatment efficacy. AI and surgical robots can also assist surgeons in conducting gastrointestinal operations. While the advancement and new opportunities are exciting, the responsibility and liability issues of AI-assisted diagnosis and management need much deliberations. Keywords
artificial intelligence; endoscopy; robotics; gastrointestinal diseases
Background
labor-intensive, repetitive, and mundane tasks of clinicians. Gastroenterology is a field that AI can make a significant impact. This is because diagnosis of gastrointestinal conditions relies much on image-based investigations (endoscopy and radiology). Taking digestive tract cancer as an example, AI-assisted image analysis aids the detection of gastrointestinal neoplasia during endoscopy, provides optical biopsy to determine the nature of lesions, integrates genomic and epigenetic data to provide new classification of cancers, and provides evidence-based suggestions for optimal therapies. Furthermore, AIassisted surgical operations, through semi-automated and automated robotic surgery, will obviate some part of surgical procedures to be performed by surgeons.
The era of artificial intelligence (AI) has arrived in medicine, penetrating various specialties. Deep learning algorithms enable highly sensitive and specific diagnosis of diabetic retinopathy [1]. Breast cancer screening using mammography can be performed by machine-learning devices, saving much time for radiologists [2]. Automated classification of skin conditions using convoluted neural network (CNN) program in smart phones enables dermatologists to make vital diagnosis from a distance [3]. Neural network a
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