Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions.

Area of Science:

• Gastroenterology and Hepatology
• Medical Imaging
• Artificial Intelligence in Medicine

Background:

• Pancreaticobiliary endoscopy is crucial for diagnosing and treating diseases but faces challenges like operational difficulty and diagnostic errors.
• Existing methods may not fully meet clinical needs, highlighting a gap in current diagnostic and therapeutic capabilities.

Purpose of the Study:

• To provide a comprehensive overview of current artificial intelligence (AI) applications in pancreaticobiliary endoscopy.
• To explore the potential of AI in enhancing diagnostic accuracy, treatment efficiency, and patient outcomes.
• To propose future research directions for AI integration in this field.

Main Methods:

• Review of existing literature on AI applications in pancreaticobiliary endoscopy.
• Analysis of AI's role in endoscopic ultrasound (EUS) image interpretation, pathology analysis, and procedure guidance.
• Discussion of AI's impact on operational difficulty assessment and prognosis prediction.

Main Results:

• AI significantly improves diagnostic and treatment efficiency and quality in pancreaticobiliary endoscopy.
• AI assists in diagnosis, differential diagnosis, pathology assessment, and real-time procedure guidance.
• AI aids in evaluating operational difficulty, predicting complications, and forecasting patient prognosis.

Conclusions:

• AI offers substantial advancements for pancreaticobiliary endoscopy, addressing current clinical limitations.
• Future development should focus on data quality and algorithmic interpretability for seamless AI integration.
• AI integration promises to revolutionize pancreaticobiliary endoscopy, improving patient care and outcomes.