Artificial Intelligence–Assisted Endoscopy in Diagnosis of Gastrointestinal Tumors: A review of Systematic Reviews and Meta-Analyses

Artificial Intelligence–Assisted Endoscopy in Diagnosis of Gastrointestinal Tumors: A review of Systematic Reviews and Meta-Analyses

Olivier Sibomana, Sulymon Saka, Marie Grace Uwizeyimana, Alex Mwangi Kihunyu , Abraham Obianke, Samuel Oluwo Damilare, Lewis Tem Bueh, Beloved of God Agbelemoge, Richard Omoefe Oveh
Department of General Medicine and Surgery, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda; Department of Otolaryngology, Irrua Specialist Teaching Hospital, Irrua Edo, Nigeria; University of Nairobi, Nairobi, Kenya; Department of Medicine and Surgery, College of Medical Sciences, Faculty of Clinical Science, Ambrose Alli University, Edo State, Nigeria; Department of Dentistry and Dental surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria; Department of Computer Engineering, University of Buea, Cameroon; Department of Medicine and Surgery, Ambrose Alli University Ekpoma, Edo State, Nigeria; Department of information and communication Technology, Faculty of computing, University of Delta, Delta state, Nigeria

Abstract
Introduction: Early and accurate detection of gastrointestinal (GI) tumors is crucial to reduce their associated high morbidity, mortality, and financial burden. Recent advancements in health technology have led to the development of AI-assisted endoscopy, which has been studied as potential tool to increase early and accurate GI tumors detection. This review aims to summarize systematic reviews and meta-analyses on AI-assisted endoscopy in diagnosing GI tumors.

Methods: A comprehensive search was performed using PubMed/MEDLINE, Google Scholar, DOAJ, AJOL, and the Cochrane Library, along with manual searches. Systematic reviews and meta-analyses focusing on AI-assisted endoscopy for GI tumor diagnosis were included based on pre-specified criteria. Relevant data were extracted and summarized to present different metrics AI endoscopy in GI tumors diagnosis.

Results: From 569 studies identified, 23 systematic reviews with meta-analyses were included. 6 focused on detection rates, and 17 examined diagnostic accuracy. AI-assisted endoscopy showed a significantly higher detection rate for GI tumors compared to conventional endoscopy. Additionally, AI-assisted endoscopy showed high diagnostic accuracy in detecting different types of GI tumors. However, variability in detection rate and accuracy results across different AI algorithms and studies was noted.

Conclusion: AI-assisted endoscopy offers high diagnostic accuracy and improved tumor detection than conventional endoscopy. Meanwhile, rigorous evaluation of AI models is necessary to ensure clinical reliability. Additionally, AI should enhance clinical decision-making rather than replace clinicians. Addressing ethical concerns and further research are essential for optimizing AI in healthcare.

Keywords: Artificial intelligence, endoscopy, GI tumors, cancer