Ularick Poltas
Ulcerative Colitis (UC) is a Chronic Inflammatory Bowel Disease (IBD) characterized by inflammation of the colon and rectum. The diagnosis and
treatment of UC can be complex due to its unpredictable nature, which can vary widely in severity and response to treatment. Traditional diagnostic
methods, such as endoscopy and biopsy, are invasive and often associated with discomfort for the patient. Moreover, treatment strategies can
involve a trial-and-error approach, given the variability in individual responses to medications. In recent years, the advent of Artificial Intelligence
(AI) has shown promise in transforming the landscape of UC management. AI technologies, including Machine Learning (ML) and Deep Learning
(DL), are increasingly being utilized to enhance diagnostic accuracy, personalize treatment plans, and predict disease course, thereby improving
patient outcomes and reducing healthcare costs. AI's role in UC diagnosis primarily revolves around enhancing the capabilities of imaging and
endoscopy. Traditional endoscopic procedures involve visual assessment by a gastroenterologist to identify inflammation, ulcers, and other
abnormalities. AI algorithms, particularly those based on DL, have been developed to assist in the interpretation of endoscopic images. These
algorithms can detect subtle mucosal changes that may be indicative of UC with higher accuracy and consistency compared to human observers.
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