4.6 Review

Artificial intelligence in gastroenterology and hepatology: Status and challenges

Journal

WORLD JOURNAL OF GASTROENTEROLOGY
Volume 27, Issue 16, Pages 1664-1690

Publisher

BAISHIDENG PUBLISHING GROUP INC
DOI: 10.3748/wjg.v27.i16.1664

Keywords

Artificial intelligence; Gastroenterology; Hepatology; Status; Challenges

Funding

  1. Zhejiang Medical and Health Science and Technology Project [2019321842]
  2. National Natural Science Foundation of China [81827804]
  3. Zhejiang Clinical Research Center of Minimally Invasive Diagnosis and Treatment of Abdominal Diseases [2018E50003]

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Artificial intelligence has made significant breakthroughs in clinical medicine, especially in medical image analysis, playing a crucial role in diagnosing and treating diseases. Despite facing challenges in application, cooperation between doctors and researchers can overcome these challenges to develop more accurate AI tools for improved clinical applications.
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.

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