Artificial Intelligence in GI and Hepatology
Innovative applications for alternative gastrointestinal conditions
Given the fervor and excitement, as well as the outcomes associated with AI-based colorectal screening, it is not surprising these techniques have been expanded to other gastrointestinal conditions. At this time, all of these are fledgling, mostly single-center tools, not yet ready for widespread adoption. Nonetheless, these represent a potentially important step forward for difficult-to-manage gastrointestinal diseases.
Machine learning CADe systems have been developed to help identify early Barrett’s neoplasia, depth and invasion of gastric cancer, as well as lesion detection in small bowel video capsule endoscopy.8-10 Endoscopic retrograde cholangiopancreatography (ERCP)-based applications for cholangiocarcinoma and indeterminate stricture diagnosis have also been studied.11 Additional AI-based algorithms have been employed for complex procedures such as endoscopic submucosal dissection (ESD) or peroral endoscopic myotomy (POEM) to delineate vessels, better define tissue planes for dissection, and visualize landmark structures.12,13 Furthermore, AI-based scope guidance/manipulation, bleeding detection, landmark identification, and lesion detection have the potential to revolutionize endoscopic training and education. The impact that generative AI can potentially have on clinical practice is also an exciting prospect that warrants further investigation.
Artificial intelligence adoption in clinical practice
Clinical practice with regard to AI and colorectal cancer screening largely mirrors the disconnect in the current literature, with “believers” and “non-believers” as well as innovators and early adopters alongside laggards. In our own academic practices, we continue to struggle with the adoption and standardized implementation of AI-based colorectal cancer CADe systems, despite the RCT data showing positive results. It is likely that AI uptake will follow the technology predictions of Amara’s Law — i.e., individuals tend to overestimate the short-term impact of new technologies while underestimating long-term effects. In the end, more widespread adoption in community practice and larger scale real-world clinical outcomes studies are likely to determine the true impact of these exciting technologies. For other, less established AI-based tools, more data are currently required.
Conclusions
Ultimately, AI-based algorithms are likely here to stay, with continued improvement and evolution to occur based on provider feedback and patient care needs. Current tools, while not all-encompassing, have the potential to dramatically change the landscape of endoscopic training, diagnostic evaluation, and therapeutic care. It is critically important that relevant stakeholders, both endoscopists and patients, be involved in future applications and design to improve efficiency and quality outcomes overall.
Dr. McCarty is based in the Lynda K. and David M. Underwood Center for Digestive Disorders, Houston Methodist Hospital. Dr. Mansour is based in the section of gastroenterology, Baylor College of Medicine, Houston. Dr. McCarty reports no conflicts of interest. Dr. Mansour reports having been a consultant for Iterative Health.
References
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