Gastroenterology and Artificial Intelligence: 4th Annual Global Artificial Intelligence Summit | August 2022-AI Solutions in Pancreas, Esophageal Motility, and Capsule Endoscopy

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This document discusses the use of artificial intelligence (AI) solutions in the fields of pancreas, esophageal motility, and capsule endoscopy in gastroenterology. The author emphasizes the need to define clinical needs and improve quality of care in these areas. <br /><br />For pancreatic conditions, such as cysts and adenocarcinoma, current care is deemed inefficient and ineffective. AI tools can assist in the identification and classification of pancreas cysts, enabling early detection of at-risk cysts and timely intervention to prevent the development of pancreatic cancer. Machine learning algorithms can also analyze electronic health record data to predict the likelihood of developing pancreatic cancer, helping clinicians make more informed decisions and recommend appropriate imaging tests.<br /><br />In esophageal motility disorders, delayed diagnosis and the inability to interpret diagnostic tests lead to inefficient and ineffective care. AI tools, including computer vision in endoscopy, can analyze still frames from endoscopic procedures to identify features associated with motility disorders. This can aid in the early detection and prompt management of these conditions.<br /><br />In capsule endoscopy, AI technology can address the problem of missed lesions and the time-consuming process of reviewing studies. Machine learning algorithms can improve the efficiency and effectiveness of small bowel capsule endoscopy by accurately detecting and analyzing obscure gastrointestinal bleeding.<br /><br />Overall, the document highlights the potential of AI in transforming gastroenterology by addressing clinical needs, improving the quality of care, and potentially disrupting the management of common conditions.

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Rajesh Keswani, MD

Keywords

artificial intelligence

pancreas

esophageal motility

capsule endoscopy

gastroenterology

clinical needs

pancreatic cancer

machine learning algorithms

endoscopy

gastrointestinal bleeding