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Diagnostics
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Artificial Intelligence and Big Data in Digestive Healthcare
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Artificial Intelligence and Big Data in Digestive Healthcare

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 970

Special Issue Editors

Dr. Hélder Cardoso

E-Mail Website
Guest Editor
1.Gastroenterology Department, Centro Hospitalar Universitário de São João, 4200-319 Porto, Portugal
2. Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
Interests: gastroenterology; hepatology; endoscopy; capsule endoscopy; enteroscopy; applied artificial intelligence; liver cancer; inflammatory bowel diseases
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Mascarenhas

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, 4200-319 Porto, Portugal
2. Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
Interests: inflammatory bowel disease; applied artificial intelligence; capsule endoscopy; neurogastroenterology; coloproctology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence (AI) and big data are revolutionizing healthcare, particularly in the field of digestive medicine. This Special Issue, “Artificial Intelligence and Big Data in Digestive Healthcare”, seeks to explore the transformative potential of these technologies in diagnosing, treating, and managing digestive disorders.

Digestive healthcare faces complex challenges, from identifying subtle patterns in imaging to predicting disease progression and optimizing treatment strategies. The integration of AI techniques, such as machine learning and natural language processing, with big data analytics offers groundbreaking opportunities to address these challenges. For instance, AI-powered systems can enhance the accuracy of endoscopic diagnostics, streamline the analysis of electronic health records (EHRs), and improve personalized care through predictive modeling.

This issue will showcase cutting-edge research and practical applications in areas such as AI-assisted imaging for the early detection of colorectal cancer, automated analysis of histopathological data, and predictive algorithms for inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Additionally, it will delve into the role of big data in aggregating and analyzing genomic, microbiome, and lifestyle data to inform precision medicine.

Contributions to this issue should also address ethical considerations, including data privacy, algorithmic transparency, and equitable access to AI-driven tools in various healthcare settings. Researchers, clinicians, and industry leaders should share insights on overcoming barriers to the adoption of such tools, such as the integration of AI into clinical workflows and the training of healthcare professionals.

By bridging technological innovation with clinical practice, this Special Issue aims to pave the way for smarter, more efficient, and patient-centered approaches in digestive healthcare. Readers will gain a comprehensive understanding of the current landscape, emerging trends, and future directions in this dynamic intersection of AI, big data, and digestive healthcare.

Dr. Hélder Cardoso
Prof. Dr. Miguel Mascarenhas
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 short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Diagnostics 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

  • artificial intelligence
  • big data
  • digestive system diseases
  • digestive system neoplasms
  • electronic health records
  • endoscopy
  • precision medicine

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Published Papers (2 papers)

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29 pages, 3428 KB  
Article
Scalable Unimodal and Multimodal Deep Learning for Multi-Label Chest Disease Detection: A Comparative Analysis
by Diğdem Orhan, Murat Ucan, Reda Alhajj and Mehmet Kaya
Diagnostics 2026, 16(5), 734; https://doi.org/10.3390/diagnostics16050734 - 1 Mar 2026
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Abstract
Background/Objectives: Early and accurate diagnosis of chest diseases is a critical challenge in clinical practice, particularly in scenarios where multiple pathologies may coexist. While deep learning-based medical image analysis has shown promising results, most existing studies rely on unimodal data and fixed-scale [...] Read more.
Background/Objectives: Early and accurate diagnosis of chest diseases is a critical challenge in clinical practice, particularly in scenarios where multiple pathologies may coexist. While deep learning-based medical image analysis has shown promising results, most existing studies rely on unimodal data and fixed-scale datasets, limiting their generalizability and clinical relevance. In this study, we present a comprehensive comparative analysis of unimodal and multimodal deep learning models for multi-label chest disease classification using chest X-ray images and associated clinical metadata. Methods: A total of twelve models were developed based on three widely used convolutional neural network architectures—ResNet50, EfficientNetB3, and DenseNet121—under both unimodal (image-only) and multimodal (image + clinical data) configurations. To systematically investigate the impact of data scale, experiments were conducted on two distinct versions: the Random Sample of NIH Chest X-ray Dataset and the NIH Chest X-ray Dataset, containing 5606 and 121,120 samples, respectively. Model performance was evaluated using label-based Area Under the Receiver Operating Characteristic Curve (AUROC) metrics. Results: Experimental results demonstrate that multimodal fusion consistently outperforms unimodal approaches across all architectures and data scales, with more pronounced improvements observed in large-scale settings. Furthermore, increasing data volume leads to improved generalization and reduced performance variance, particularly for rare pathologies. Conclusions: These findings highlight the effectiveness of multimodal, multi-label learning in enhancing diagnostic accuracy and support the development of robust clinical decision support systems for chest disease assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data in Digestive Healthcare)
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10 pages, 3020 KB  
Article
Robotic Capsule Endoscopy: Simultaneous Gastric and Enteric Evaluation in Real-World Practice
by Hélder Cardoso, Miguel Mascarenhas, Joana Mota, Miguel Martins, Maria João Almeida, Joana Frias, Catarina Cardoso Araújo, Francisco Mendes, Margarida Marques, Patrícia Andrade and Guilherme Macedo
Diagnostics 2026, 16(2), 334; https://doi.org/10.3390/diagnostics16020334 - 20 Jan 2026
Viewed by 338
Abstract
Background/Objectives: Robotic capsule endoscopy (RCE) is an emerging technology that combines magnetically controlled gastric navigation with conventional capsule enteroscopy (CE), enabling a minimally invasive, comprehensive evaluation of the upper- and mid-gastrointestinal tract. This study aimed to characterize the real-world implementation and diagnostic [...] Read more.
Background/Objectives: Robotic capsule endoscopy (RCE) is an emerging technology that combines magnetically controlled gastric navigation with conventional capsule enteroscopy (CE), enabling a minimally invasive, comprehensive evaluation of the upper- and mid-gastrointestinal tract. This study aimed to characterize the real-world implementation and diagnostic performance of RCE in a European tertiary referral center. Methods: A retrospective, single-center analysis was conducted on adult patients (≥18 years) who underwent RCE (Omom RC) between June 2023 and July 2025. Eligible patients had a clinical indication for small bowel CE and a concurrent requirement for diagnostic gastroscopy or reassessment of known gastric lesions. The RCE protocol comprised an initial robotic-guided gastric examination followed by passive transit through the small bowel. Results: A total of 85 patients were included (52% female), with a median age of 49 years (IQR 40–64). The most common indications were suspected or established inflammatory bowel disease (57%) and iron deficiency anemia (31%). Gastric preparation was rated at least fair in 98% of cases, with good preparation in 38%. Median gastric transit time was 74 min (IQR 35–106). Relevant gastric findings were identified in 39 cases (46%), namely polyps (18%) and angiectasias (8%, including one with active bleeding), in addition to signs of chronic gastritis. Thirteen patients underwent subsequent endoscopy, resulting in seven therapeutic procedures. Small bowel findings were present in 60 patients (71%), including P3 (active bleeding) in 3% and P2 lesions (angiectasias, ulcers, tumors, varices) in 39%. One moderate adverse event occurred: small bowel capsule retention in a patient with multifocal neuroendocrine tumor and ileostomy, requiring endoscopic intervention. Conclusions: Robotic capsule endoscopy is a feasible tool for dual-region gastrointestinal evaluation. It enables high-quality gastric visualization, facilitates early detection of clinically actionable lesions, and maintains the diagnostic yield expected from standard small bowel CE. These findings support the integration of RCE into diagnostic pathways for patients requiring simultaneous gastric and small bowel assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data in Digestive Healthcare)
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