The evidence from various studies is mixed but general consensus is that AI devices can enhance adenoma detection rates (ADR) during colonoscopy, benefiting endoscopists at all levels of experience.
Clinical Trials vs. Real-World Results: While randomized clinical trials worldwide show an increase in ADR with AI use during colonoscopy, real-world results are varied. Some studies indicate improvement, while others do not. This discrepancy may be due to differences in study designs and implementation methods.
Impact on Surveillance and Procedure Quality: AI use in colonoscopy has led to an increase in the proportion of patients requiring intensive surveillance, potentially improving cancer prevention but also adding to patient burden and healthcare costs. However, the impact of AI on procedure times and non-neoplastic detection rates is not significant.
Perceptions and Expectations of Endoscopists: The effectiveness of AI in colonoscopy can be influenced by endoscopists’ perceptions and expectations. Some may view AI as a challenge to their expertise, while others may rely too heavily on it, potentially affecting the quality of the examination.
Experience Level of Endoscopists: AI seems to be particularly beneficial for less experienced endoscopists, helping them detect adenomas more effectively. However, experienced endoscopists can also benefit from AI as an adjunctive tool.
Future of AI in Colonoscopy: Ongoing research aims to improve AI algorithms, increasing their accuracy and quality for all users. This advancement could lead to new standards and more consistent outcomes in both clinical trials and real-world applications. AI might also play a role in health equity, though it requires careful training and diverse data to avoid biases.
The article underscores the potential of AI in enhancing colonoscopy procedures, while also highlighting the need for further research and refinement of AI tools to maximize their benefits in clinical practice.