Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Deep Learning

Background:

• Deep learning algorithms achieve human-level performance in various domains.
• Medical image analysis faces challenges including data heterogeneity, multi-modality, and rare disease detection.
• Endoscopic procedures are operator-dependent, impacting clinical outcomes.

Purpose of the Study:

• To review recent advancements in artificial intelligence (AI) for endoscopic image analysis.
• To identify current unmet needs and challenges in the field.
• To outline future research directions for clinically relevant AI solutions.

Main Methods:

• Review of recent literature on AI applications in endoscopic image analysis.
• Emphasis on challenges such as data heterogeneity, generalizability, and rare disease cases.
• Identification of areas requiring further research and development.

Main Results:

• AI has shown promise in medical image analysis, but generalizability remains a key issue.
• Data heterogeneity, multi-modality, and the need for expert input are significant hurdles.
• Current AI methods often lack robustness across diverse patient populations and disease presentations.

Conclusions:

• There is a critical need for more generalizable and robust AI solutions in endoscopic image analysis.
• Addressing data heterogeneity and incorporating expert knowledge are crucial for clinical translation.
• Future research should focus on developing complex AI solutions to enhance patient care and outcomes in endoscopy.