The machine is tasked with finding its own structure and patterns from the set of objects. In unsupervised learning, machines are given data inputs that are not explicitly paired to labels or outputs. Linear and logistic regression, which are often employed in clinical research, are examples of supervised machine learning because they produce a regression function that correlates inputs to outputs based on observed data. The machine learns a function to map the inputs to outputs, which can then be applied toward new examples. In supervised learning, a machine is trained with data that contain pairs of inputs and outputs. Machine learning is often classified into two categories - supervised and unsupervised learning. Examples of machine learning include an application that learns to identify and discard spam emails or a thermostat that learns household temperature preferences over time. Machine learning is an artificial intelligence technique in which computers use data to improve their performance in a task without explicit instruction. Interest in the field grew over the next few decades due to the exponential increase in computational power and data volume. Research in AI began in the 1950s with the earliest applications being in board games, logical reasoning, and simple algebra. In this review, we aim to (1) provide a brief overview of artificial intelligence technology (2) describe the ways in which AI has been applied to gastroenterology thus far (3) discuss what value AI offers to this field and finally (4) comment on future directions of this technology.Īrtificial intelligence is machine intelligence that mimics human cognitive function. As the number of applications of AI in gastroenterology expands, it is important to understand the extent of our success and the hurdles that lie ahead. Research groups have shown how deep learning can assist with a variety of skills from colonic polyp detection to analysis of wireless capsule endoscopy (WCE) images. In recent years, AI tools have been designed to help physicians in performing these tasks. Gastroenterology is a field that requires physicians to perform a myriad of clinical skills, ranging from dexterous manipulation and navigation of endoscopic devices and visual identification and classification of disease to data-driven clinical decision-making. As the medical community’s understanding and acceptance of AI grows, so too does our imagination of the many ways in which it can improve patient care, expedite clinical processes, and relieve the burden of medical professionals. Over the past few decades, researchers have successfully demonstrated how AI can improve our ability to perform medical tasks, ranging from the identification of diabetic retinopathy to the diagnosis of cutaneous malignancies. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.Īrtificial intelligence (AI) has transformed information technology by unlocking large-scale, data-driven solutions to what once were time intensive problems. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications.
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