ARTIFICIAL INTELLIGENCE IN GASTROINTESTINAL SURGERY

Artificial_Intelligence_in_Gastrointestinalkapak

ARTIFICIAL INTELLIGENCE (AI) IN SMALL INTESTINE DISEASE

Necip Tolga Baran

Ankara Etlik City Hospital, Department of Gastroenterology Surgery, Ankara, Türkiye

Baran NT. Artificial Intelligence (AI) in Small Intestine Disease. Çaycı HM, ed. Artificial Intelligence (AI) in Gastrointestinal Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.53-61.

ABSTRACT

The role of the small intestine in the digestive system is significant, encompassing various established functions and possibly some yet-to-be-uncovered roles that medical professionals are still striving to fully comprehend. Accessing the small intestine with endoscopic methods presents challenges not found in other digestive organs due to its complex anatomy, complicating the detection of abnormalities with typical radiological methods. This complexity makes diagnosing conditions affecting the small intestine more challenging. The integration of artificial intelligence (AI) is transforming the management of small intestine disorders, offering greater accuracy and improving surgical and treatment strategies. AI assists healthcare professionals in identifying diseases, handling chronic conditions, making surgical choices, preparing for operations, and overseeing recovery after surgery. However, AI faces hurdles in ensuring data accuracy and reliability while gaining the confidence of medical practitioners. Ethical issues regarding data privacy must also be addressed. Despite these challenges, AI holds significant promise for advancing healthcare and improving outcomes for digestive disorders. Emerging technologies, such as the combination of AI and augmented reality in microbiome research, are anticipated to revolutionize the treatment of small bowel diseases by enhancing treatment efficiency and tailoring therapies to meet the specific needs of each patient.

Keywords: Artificial intelligence; Intestine small; Capsule endoscopy; Deep learning; Predictive learning models; Robotic surgical procedures; Disease management; Precision medicine; Machine learning

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