ArticleTherapeutic advances in gastroenterology2022
Key research questions for implementation of artificial intelligence in capsule endoscopy.
Article in Therapeutic advances in gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automatic Evaluation of Small Bowel Cleanliness in Capsule Endoscopy: A Comparative Study of Four Preparations using Artificial Intelligence.Endoscopy international open · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).BMC gastroenterology · 2024Article
- From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?Diagnostics (Basel, Switzerland) · 2024Review
- The Role of Capsule Endoscopy in the Diagnosis and Management of Small Bowel Tumors: A Narrative Review.Cancers · 2024Review
- Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence.Gut and liver · 2023Article
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Results: Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). Conclusion: In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.