Journal of Gastroenterology and Digestive Diseases

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
Reach Us +1 (629)348-3199

Rapid Communication - Journal of Gastroenterology and Digestive Diseases (2023) Volume 8, Issue 6

Emerging trends in gastrointestinal endoscopy: From artificial intelligence to robotic assisted procedures.

Prabha Sharma*

Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Punjab, India

*Corresponding Author:
Prabha Sharma
Department of Pharmacoinformatics
National Institute of Pharmaceutical Education and Research, Punjab, India

Received: 12-Oct-2023, Manuscript No. JGDD-23-120819; Editor assigned: 16-Oct-2023, Pre QC No. JGDD-23-120819 (PQ); Reviewed: 31-Oct-2023, QC No. JGDD-23-120819; Revised: 03-Nov-2023, Manuscript No. JGDD-23-120819 (R); Published: 09- Nov-2023, DOI: 10.35841/ jgdd -8.6.175

Citation: Sharma P. Emerging trends in gastrointestinal endoscopy: From artificial intelligence to robotic assisted procedures. J Gastroenterol Dig Dis.2023;8(6):175

Visit for more related articles at Journal of Gastroenterology and Digestive Diseases


Gastrointestinal endoscopy has evolved significantly over the years, transforming from a diagnostic tool to a therapeutic modality for various gastrointestinal disorders. Recent advancements in technology, particularly in the realms of artificial intelligence (AI) and robotics, are revolutionizing the field of gastrointestinal endoscopy. These emerging trends not only enhance the accuracy of diagnosis but also contribute to the development of minimally invasive procedures, improving patient outcomes and overall healthcare efficiency. Artificial intelligence has made significant inroads into healthcare, and gastrointestinal endoscopy is no exception. AI algorithms are being integrated into endoscopic systems to aid in lesion detection, characterization, and even pathology recognition. One of the key applications of AI in this field is the automatic detection of polyps during colonoscopies, a task that traditionally relies on the expertise of the endoscopist [1, 2].

Computer-aided detection (CAD) systems use machine learning algorithms to analyze endoscopic images in realtime, flagging potential abnormalities for further review. This not only improves the efficiency of the endoscopist but also enhances the overall accuracy of polyp detection, reducing the risk of missed lesions. As these AI algorithms continue to be refined and validated through clinical studies, they hold the promise of becoming invaluable tools for early detection and intervention in gastrointestinal diseases. Beyond lesion detection, AI is also being applied to enhance diagnostic capabilities in gastrointestinal endoscopy [3, 4].

The integration of AI into the diagnostic workflow has the potential to reduce subjectivity, standardize interpretations, and improve overall diagnostic accuracy. Robotic-assisted procedures have gained momentum in various surgical specialties, and gastrointestinal endoscopy is gradually adopting this technology to further refine and expand minimally invasive interventions. Robotic-assisted endoscopy allows for enhanced dexterity and precision, offering surgeons a greater range of motion and control during procedures. This is particularly beneficial in complex interventions such as endoscopic submucosal dissection (ESD) and endoscopic mucosal resection (EMR) [5, 6].

The robotic platform provides a stable and articulated system that enables precise maneuvering of endoscopic instruments. This can be especially advantageous in challenging anatomical locations or when performing intricate procedures requiring intricate movements. As robotic technology continues to advance, it is expected that these systems will become more widely adopted, contributing to the growth of minimally invasive interventions in gastrointestinal endoscopy. While the integration of AI and robotics in gastrointestinal endoscopy holds immense promise, it is not without challenges. The initial cost of acquiring and implementing these technologies can be a barrier for some healthcare institutions. Additionally, there is a learning curve associated with adopting new technologies, necessitating training and skill development among healthcare professionals [7, 8].

Moreover, issues related to data privacy, ethical considerations, and the need for robust clinical validation of AI algorithms must be addressed to ensure the responsible and safe implementation of these technologies. As the field progresses, collaborative efforts between healthcare providers, technology developers, and regulatory bodies will be crucial in overcoming these challenges and ensuring the widespread adoption of these emerging trends [9, 10].


Gastrointestinal endoscopy is undergoing a transformative phase with the integration of artificial intelligence and roboticassisted procedures. These technologies are reshaping the landscape of diagnostic and therapeutic interventions, offering improved precision, efficiency, and patient outcomes. As AI algorithms become more sophisticated and robotic platforms continue to evolve, the future of gastrointestinal endoscopy holds the promise of enhanced diagnostic accuracy, expanded therapeutic capabilities, and an overall improvement in the quality of patient care.


  1. Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Annals of internal medicine. 2018;169(6):357-66.
  2. Indexed at, Google Scholar, Cross Ref

  3. Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology. 2018;154(8):2027-9.
  4. Indexed at, Google Scholar, Cross Ref

  5. Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Computer methods and programs in biomedicine. 2015;122(3):341-53.
  6. Indexed at, Google Scholar, Cross Ref

  7. Lee BI, Matsuda T. Estimation of invasion depth: the first key to successful colorectal ESD. Clinical Endoscopy. 2019;52(2):100-6.
  8. Indexed at, Google Scholar, Cross Ref

  9. Mori Y, Kudo SE, Chiu PW, et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy. 2016;48(12):1110-8.
  10. Google Scholar

  11. Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology. 2016;150(7):1531-2.
  12. Indexed at, Google Scholar, Cross Ref

  13. Dent J. Barrett's esophagus: a historical perspective, an update on core practicalities and predictions on future evolutions of management. Journal of gastroenterology and hepatology. 2011;26:11-30.
  14. Indexed at, Google Scholar, Cross Ref

  15. Phoa KN, Pouw RE, Bisschops R, et al. Multimodality endoscopic eradication for neoplastic Barrett oesophagus: results of an European multicentre study (EURO-II). Gut. 2015 Mar 2:gutjnl-2015.
  16. Indexed at, Google Scholar, Cross Ref

  17. Abrams JA, Kapel RC, Lindberg GM, et al. Adherence to biopsy guidelines for Barrett's esophagus surveillance in the community setting in the United States. Clinical Gastroenterology and Hepatology. 2009;7(7):736-42.
  18. Google Scholar

  19. Swager AF, van der Sommen F, Klomp SR, et al. Computer-aided detection of early Barrett’s neoplasia using volumetric laser endomicroscopy. Gastrointestinal endoscopy. 2017;86(5):839-46.
  20. Indexed at, Google Scholar, Cross Ref

Get the App