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Cancers
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The Applications of Artificial Intelligence in Gastroenterology
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The Applications of Artificial Intelligence in Gastroenterology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5008

Special Issue Editors

Prof. Dr. Marcello F. Maida

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Guest Editor
Department of Medicine and Surgery, University of Enna ‘Kore’, Enna, Italy
Interests: gastroenterology; endoscopy; gastrointestinal oncology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ciro Celsa

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Guest Editor
1. Gastroenterology and Hepatology Unit, Department of Health Promotion, Mother & Child Care, Internal Medicine & Medical Specialties, University of Palermo, Palermo, Italy
2. Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, Du Cane Road, London W120HS, UK
Interests: hepatocellular carcinoma; cirrhosis; portal hypertension; gastrointestinal oncology; immunotherapy
Dr. Louis H. S. Lau

E-Mail Website
Guest Editor
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
Interests: advanced endoscopy; gastrointestinal bleeding; big data research and artificial intelligence in medicine

Special Issue Information

Dear Colleagues,

The wave of artificial intelligence (AI) is sweeping all fields of medicine, including gastroenterology and gastrointestinal endoscopy. 

To date, AI represents a promising solution to overcome human biases, provide diagnostic support, improve clinical decision-making, and standardize outcome assessment in scientific research.
An extensive body of evidence regarding AI in gastroenterology is currently available, but the transition of these technologies to the bedside is still in progress. 

This Special Issue aims to summarize and evaluate the applications and recent advances of artificial intelligence in gastroenterology, addressing their effectiveness, limitations, and future perspectives.

Topics covered

  • Lesion detection during colonoscopy (ADR, AMR, etc.);
  • Quality assessment of colonoscopy (bowel cleansing, mucosal exposure quality, etc.).
  • Inflammatory bowel diseases (activity assessment and dysplasia);
  • Upper GI (Barrett, early gastric cancer, etc.);
  • Capsule endoscopy;
  • EUS;
  • Hepatology;
  • Pancreatology.

Prof. Marcello Maida
Dr. Ciro Celsa
Dr. Louis H. S. Lau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • gastroenterology
  • endoscopy
  • inflammatory bowel diseases

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Review

11 pages, 734 KiB  
Review
AI and Polyp Detection During Colonoscopy
by Marco Spadaccini, Maddalena Menini, Davide Massimi, Tommy Rizkala, Roberto De Sire, Ludovico Alfarone, Antonio Capogreco, Matteo Colombo, Roberta Maselli, Alessandro Fugazza, Luca Brandaleone, Antonio Di Martino, Daryl Ramai, Alessandro Repici and Cesare Hassan
Cancers 2025, 17(5), 797; https://doi.org/10.3390/cancers17050797 - 26 Feb 2025
Cited by 1 | Viewed by 1281
Abstract
Colorectal cancer (CRC) prevention depends on effective colonoscopy; yet variability in adenoma detection rates (ADRs) and missed lesions remain significant hurdles. Artificial intelligence-powered computer-aided detection (CADe) systems offer promising advancements in enhancing polyp detection. This review examines the role of CADe in improving [...] Read more.
Colorectal cancer (CRC) prevention depends on effective colonoscopy; yet variability in adenoma detection rates (ADRs) and missed lesions remain significant hurdles. Artificial intelligence-powered computer-aided detection (CADe) systems offer promising advancements in enhancing polyp detection. This review examines the role of CADe in improving ADR and reducing adenoma miss rates (AMRs) while addressing its broader clinical implications. CADe has demonstrated consistent improvements in ADRs and AMRs; largely by detecting diminutive polyps, but shows limited efficacy in identifying advanced adenomas or sessile serrated lesions. Challenges such as operator deskilling and the need for enhanced algorithms persist. Combining CADe with adjunctive techniques has shown potential for further optimizing performance. While CADe has standardized detection quality; its long-term impact on CRC incidence and mortality remains inconclusive. Future research should focus on refining CADe technology and assessing its effectiveness in reducing the global burden of CRC. Full article
(This article belongs to the Special Issue The Applications of Artificial Intelligence in Gastroenterology)
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15 pages, 735 KiB  
Review
Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care
by Nicola Pugliese, Arianna Bertazzoni, Cesare Hassan, Jörn M. Schattenberg and Alessio Aghemo
Cancers 2025, 17(5), 722; https://doi.org/10.3390/cancers17050722 - 20 Feb 2025
Cited by 1 | Viewed by 980
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. [...] Read more.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as “hallucinations”. This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations. Full article
(This article belongs to the Special Issue The Applications of Artificial Intelligence in Gastroenterology)
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20 pages, 804 KiB  
Review
The Application of Large Language Models in Gastroenterology: A Review of the Literature
by Marcello Maida, Ciro Celsa, Louis H. S. Lau, Dario Ligresti, Stefano Baraldo, Daryl Ramai, Gabriele Di Maria, Marco Cannemi, Antonio Facciorusso and Calogero Cammà
Cancers 2024, 16(19), 3328; https://doi.org/10.3390/cancers16193328 - 28 Sep 2024
Cited by 2 | Viewed by 2088
Abstract
Large language models (LLMs) are transforming the medical landscape by enhancing access to information, diagnostics, treatment customization, and medical education, especially in areas like Gastroenterology. LLMs utilize extensive medical data to improve decision-making, leading to better patient outcomes and personalized medicine. These models [...] Read more.
Large language models (LLMs) are transforming the medical landscape by enhancing access to information, diagnostics, treatment customization, and medical education, especially in areas like Gastroenterology. LLMs utilize extensive medical data to improve decision-making, leading to better patient outcomes and personalized medicine. These models are instrumental in interpreting medical literature and synthesizing patient data, facilitating real-time knowledge for physicians and supporting educational pursuits in medicine. Despite their potential, the complete integration of LLMs in real-life remains ongoing, particularly requiring further study and regulation. This review highlights the existing evidence supporting LLMs’ use in Gastroenterology, addressing both their potential and limitations. Recent studies demonstrate LLMs’ ability to answer questions from physicians and patients accurately. Specific applications in this field, such as colonoscopy, screening for colorectal cancer, and hepatobiliary and inflammatory bowel diseases, underscore LLMs’ promise in improving the communication and understanding of complex medical scenarios. Moreover, the review discusses LLMs’ efficacy in clinical contexts, providing guideline-based recommendations and supporting decision-making processes. Despite these advancements, challenges such as data completeness, reference suitability, variability in response accuracy, dependency on input phrasing, and a lack of patient-generated questions underscore limitations in reproducibility and generalizability. The effective integration of LLMs into medical practice demands refinement tailored to specific medical contexts and guidelines. Overall, while LLMs hold significant potential in transforming medical practice, ongoing development and contextual training are essential to fully realize their benefits. Full article
(This article belongs to the Special Issue The Applications of Artificial Intelligence in Gastroenterology)
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