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Exploring Artificial Intelligence in Precision Medicine for Gastrointestinal Diseases
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Exploring Artificial Intelligence in Precision Medicine for Gastrointestinal Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Gastroenterology & Hepatopancreatobiliary Medicine".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 1174

Special Issue Editor

Dr. Vincenzo Davide Palumbo

E-Mail Website
Guest Editor
Triolo-Zancla Hospital, 90133 Palermo, Italy
Interests: laparoscopic surgery; colorectal surgery; abdominal surgery; minimally invasive surgery; regenerative medicine; new technology in medicine

Special Issue Information

Dear Colleagues,

AI has a transformative impact on medicine in several key areas: diagnostics, personalized medicine, predictive analytics, drug discovery, virtual health assistants, robotic surgery, and clinical decision support.

In gastroenterology, AI is making significant strides through various applications:

  • Endoscopic Image Analysis: AI algorithms analyze images and videos from endoscopies to identify abnormalities such as polyps or tumors, improving the diagnostic accuracy and reducing missed detections.
  • Colorectal Cancer Screening: AI systems enhance the detection of colorectal cancer during colonoscopies by automatically identifying suspicious lesions, which can lead to earlier interventions.

In particular, the progress of endoscopy in the pancreaticobiliary field has been remarkable. The diagnosis of various pancreaticobiliary diseases (early-stage pancreaticobiliary cancer, early-stage chronic pancreatitis, etc.), which were previously difficult to diagnose, has become more accurate with the progress of ERCP and EUS. Moreover, the recent development of cholangioscopy and pancreatoscopy has provided further possibilities with high diagnostic ability. On the other hand, endoscopic treatment has also made significant progress. Therapeutic indications have expanded steadily, and completely new therapies are being developed and implemented. In particular, the advancement of interventional EUS has revolutionized endoscopic treatment for pancreaticobiliary disease. In this Special Issue, we invite authors to submit papers on clinical advances in biliary and pancreatic endoscopy in terms of both diagnosis and treatment.

Dr. Vincenzo Davide Palumbo
Guest Editor

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 short 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. Journal of Clinical Medicine 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 2600 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

  • cholangiocarcinoma
  • biliary tree stenoses
  • colorectal cancer
  • endoscopic image analysis
  • IBD
  • gastrointestinal disorders

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Published Papers (2 papers)

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Research

22 pages, 2349 KiB  
Article
A Novel Ensemble Framework for Comprehensive Early-Stage Colorectal Cancer Diagnosis, Prognosis, and Treatment: Integration of Gastroenterology-Specific Transformer Language Models and Multiple Decision Trees
by Cem Simsek, Mete Ucdal, Suayib Yalcin and Derya Karakoc
J. Clin. Med. 2025, 14(13), 4467; https://doi.org/10.3390/jcm14134467 - 23 Jun 2025
Viewed by 468
Abstract
Background: Colorectal cancer (CRC) remains a significant global health burden, with early detection and intervention crucial for improving patient outcomes. This study aims to develop and evaluate a novel proof-of-concept ensemble framework combining transformer-based language models and decision tree-based models for early-stage CRC [...] Read more.
Background: Colorectal cancer (CRC) remains a significant global health burden, with early detection and intervention crucial for improving patient outcomes. This study aims to develop and evaluate a novel proof-of-concept ensemble framework combining transformer-based language models and decision tree-based models for early-stage CRC screening, diagnosis, and prognosis. Methods: The ensemble framework consists of four key components: (1) GastroGPT, a transformer-based language model for extracting relevant data points from patient histories; (2) a decision tree-based model for assessing CRC risk and recommending colonoscopy; (3) GastroGPT for extracting data points from early CRC patients’ histories; and (4) a suite of decision tree-based models for predicting survival outcomes in early-stage CRC patients. The study employed a retrospective, observational, methodological design using simulated patient cases. Results: GastroGPT demonstrated high accuracy in extracting relevant data points from patient histories. The decision tree-based model for CRC risk assessment achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.85 (95% CI: 0.78–0.92) in predicting the need for colonoscopy. The decision tree-based models for survival prediction showed strong performance, with C-indices ranging from 0.71 to 0.75 for overall survival and disease-free survival at 24, 36, and 48 months. Conclusions: The novel ensemble framework demonstrates promising performance in early-stage CRC screening, diagnosis, and prognosis. Further research is needed to validate the models using larger, real-world datasets and to assess their clinical utility in prospective studies. Full article
(This article belongs to the Special Issue Exploring Artificial Intelligence in Precision Medicine for Gastrointestinal Diseases)
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18 pages, 1780 KiB  
Article
Evaluating the Role of Artificial Intelligence in Making Clinical Decisions for Treating Acute Pancreatitis
by Mete Ucdal, Amir Bakhshandehpour, Muhammed Bahaddin Durak, Yasemin Balaban, Murat Kekilli and Cem Simsek
J. Clin. Med. 2025, 14(12), 4347; https://doi.org/10.3390/jcm14124347 - 18 Jun 2025
Viewed by 470
Abstract
Background/Objectives: Acute pancreatitis (AP) is an illness that requires prompt diagnosis and treatment since it has the potential to become life-threatening. The American College of Gastroenterology 2024 (ACG24) guidelines offer a framework for diagnosis, severity, and treatment criteria. To assess Google Gemini application [...] Read more.
Background/Objectives: Acute pancreatitis (AP) is an illness that requires prompt diagnosis and treatment since it has the potential to become life-threatening. The American College of Gastroenterology 2024 (ACG24) guidelines offer a framework for diagnosis, severity, and treatment criteria. To assess Google Gemini application of ACG24 guidelines to Medical Information Mart for Intensive Care-III AP cases for risk, nutrition, and complication management. Methods: This observational cross-sectional study was based on 512 patients with AP who were treated in the Medical Information Mart for Intensive Care-III database from 2001 to 2012. The study compared the efficiency of Gemini in relation to the ACG24 guidelines in the three main areas of risk stratification, enteral nutrition timing, and necrotizing pancreatitis management. Enteral nutrition, according to the ACG24 guidelines, should be started within 48 h for patients who are capable, and antibiotics should only be used for confirmed infected necrosis. Results: The study included 512 patients who were divided into two groups: 213 patients with mild pancreatitis (41.6%) and 299 patients with severe pancreatitis (58.4%). The model achieved 85% accuracy for mild cases and 82% accuracy for severe cases of pancreatitis. The Acute Physiology and Chronic Health Evaluation II and Ranson scores matched the predictions of Gemini for both mild cases (p = 0.28 and p = 0.33, respectively) and severe cases (p = 0.31 and p = 0.27, respectively). The recommendations for early enteral nutrition and delayed feeding in mild cases were correct for 78% of patients, but the system suggested oral intake prematurely in 8% of severe cases. The antibiotic guideline compliance reached 82% among 156 patients with necrotizing pancreatitis, and the procedure for draining infected necrosis was correct 85% of the time. Conclusions: The Gemini model achieved 78–85% accuracy in determining pancreatitis severity and adherence to treatment guidelines but showed lower accuracy in nutrition timing compared to other parameters. Core Tip: This study evaluated the Google Gemini model in applying the American College of Gastroenterology 2024 guidelines for acute pancreatitis across 512 Medical Information Mart for Intensive Care-III cases. Results demonstrated 85% accuracy in severity classification, precise prediction of Acute Physiology and Chronic Health Evaluation II and Ranson scores, and 78–85% compliance with nutritional and necrotizing pancreatitis management guidelines. These findings suggest that artificial intelligence-based clinical decision support systems can provide rapid, consistent, and guideline-concordant recommendations, which are particularly valuable in settings with limited specialist expertise. Full article
(This article belongs to the Special Issue Exploring Artificial Intelligence in Precision Medicine for Gastrointestinal Diseases)
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