Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks.

Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.

Background: Coping strategies play a crucial role in managing inflammatory bowel disease (IBD), influencing both health-related quality of life (HRQoL) and psychological well-being. This study systematically reviews the available literature to analyze coping mechanisms in IBD populations and their impact. Methods: Relevant English-language studies published until 2023 were identified through a comprehensive search of PubMed, EMBASE, EBSCOhost, and Cochrane Library. After applying inclusion and exclusion criteria, 57 articles underwent full analysis. Results: The findings highlight the diversity of coping strategies used by individuals with IBD and emphasize the need for a nuanced approach considering factors like disease severity, duration, and individual characteristics. This review underlines the influence of coping mechanisms on QoL and indicates their potential to aid IBD management and rehabilitation. Conclusions: This study underscores the value of investigating coping strategies to promote better outcomes for individuals with IBD. Future research should explore personalized interventions that address the heterogeneity of the IBD population.

The initial clinical manifestation of acute mesenteric ischemia poses a diagnostic challenge, often leading to delays in identification and subsequent surgical intervention, contributing to adverse outcomes. Serum biomarkers, offering insights into the underlying pathophysiology, hold promise as prognostic indicators for acute mesenteric ischemia. This systematic review comprehensively explores the role of blood biomarkers in predicting clinical outcomes during follow-up for patients with mesenteric ischemia. A thorough literature search across the PubMed, Cochrane Library, and EMBASE databases yielded 33 relevant publications investigating the efficacy of serum biomarkers in predicting outcomes for mesenteric ischemia. Numerous studies underscore the utility of blood biomarkers in swiftly and accurately differentiating between causes of mesenteric ischemia, facilitating a prompt diagnosis. Elevated levels of specific biomarkers, particularly D-dimers, consistently correlate with heightened mortality risk and poorer clinical outcomes. While certain serum indicators exhibit substantial potential in associating with mesenteric ischemia, further research through rigorous human trials is imperative to enhance their consistent predictive ability during the follow-up period. This study underscores the diagnostic and prognostic significance of specific biomarkers for mesenteric ischemia, emphasizing the necessity for standardized procedures in future investigations.

Background and Objectives: Procalcitonin (PCT) is widely used to support the diagnosis of bacterial infection and sepsis, yet clinically relevant elevations also occur in multiple non-infectious conditions. This systematic review aimed to synthesize human evidence on non-infectious causes of elevated PCT and to summarize proposed pathophysiological mechanisms, with the goal of supporting context-based interpretation in clinical practice. Materials and Methods: A systematic search of PubMed/MEDLINE, Embase, Web of Science, and Scopus was performed from inception to 31 July 2025. Human studies published in English reporting quantitative PCT values in non-infectious contexts were eligible (observational studies, clinical trials, and case series with ≥5 patients). Results: Seventy-six unique studies were included. Evidence was organized across systemic inflammatory responses, cardiovascular pathology, nephrological disorders and renal replacement therapy, pulmonary diseases, gastrointestinal and hepatopancreatic diseases, autoimmune and rheumatologic conditions, neurologic and ophthalmologic conditions, onco-hematologic disorders, surgery, traumatology and transplanted patients. Across conditions, non-infectious PCT elevations were variable and frequently overlapped with ranges reported in bacterial infection, particularly in settings characterized by severe sterile inflammation and tissue injury (e.g., major surgery, trauma, shock, pancreatitis, and burns), as well as in selected malignancies with tumor-associated PCT production. Conclusions: Elevated PCT is not synonymous with bacterial infection. Interpretation should emphasize clinical context, timing, and serial trends rather than isolated thresholds, especially in high-acuity settings with strong non-infectious inflammatory stimuli. Standardized reporting of assays and sampling time points and condition-specific kinetic data are needed to refine diagnostic and stewardship algorithms.

Background: Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases. Materials and methods: We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases. Results: Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics. Conclusions: The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.