Gastroenterology

From Symptom Control to Precision Supportive Oncology: Integrating Artificial Intelligence in Supportive Oncology for Gastrointestinal Cancers.

PURPOSE OF REVIEW: Gastrointestinal (GI) cancers are among the most common malignancies worldwide and impose a substantial symptom burden from diagnosis through survivorship. Despite advances in systemic therapies and surgical approaches, patients continue to experience undertreated symptoms, psychosocial distress, financial toxicity, and fragmented supportive care. This review examines how artifi...

Real-time hallucination detection and intervention in medical LLMs via calibrated hidden-state probes.

OBJECTIVE: Most hallucination mitigation for large language models (LLMs) operates post-hoc, leaving safety-critical clinical deployment without real-time warning capability. We present a calibrated hidden-state probing pipeline that enables token-time hallucination detection under explicit false-positive-rate (FPR) constraints, making it suitable for streaming clinical deployment. METHODS: We for...

A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes.

OBJECTIVES: To develop a generalizable framework for identifying algorithmic discrimination risks arising from subgroup imbalances in machine learning...

Gut microbiome in pediatric acute pancreatitis and Crohn's disease versus irritable bowel syndrome and healthy controls.

OBJECTIVES: Pediatric acute pancreatitis (AP), Crohn's disease (CD), and irritable bowel syndrome (IBS) are associated with gut dysbiosis, but differe...

Automated model-based lesion tracking in CT: colorectal liver metastases as a use case for development and performance analysis.

OBJECTIVE: To develop and evaluate an automated CT liver lesion-tracking algorithm that matches lesions over time, detects new metastases, and reports...

Use of an AI Image Recognition-Based Smartphone App in Personalized Bowel Preparation Before Colonoscopy: Prospective Randomized Controlled Trial.

BACKGROUND: Sufficient bowel preparation is critical for increasing the quality of colonoscopy. However, current bowel preparation guidelines have lim...

Integrative multi-omics reveals MHC class II-mediated neuroinflammation and systemic metabolic dysregulation as transdiagnostic drivers in major brain disorders.

Psychiatric, neurodevelopmental, and neurodegenerative disorders, including Alzheimer's disease (AD), attention-deficit/hyperactivity disorder (ADHD),...

Improving preoperative risk stratification in colorectal liver metastases: a multi-institutional evaluation of multimodal prediction models.

Reliable prognostic models of death or liver recurrence following resection of colorectal liver metastases are critical to stratify patients for treat...

Testing the feasibility of deep learning approaches to enhance monitoring of marine macroinvertebrates: Insights from a case study using the gastropod Peringia ulvae.

Coastal ecosystems, while vital for their ecosystem services, face growing threats from climate change and human activities. Traditional methods for m...

Toward disease-associated fecal microbiome signatures for domestic mammals.

Conserved fecal microbiome signatures of intestinal diseases across domestic mammals offer a non-invasive avenue to monitor animal health and advance ...

AI-based organ weight estimation from postmortem computed tomography.

Accurate organ weight determination is essential in forensic autopsy. Postmortem computed tomography (CT) combined with Artificial Intelligence (AI)-b...

Tree-based machine learning models recover rule-based flare labels from virtual symptom diaries in pediatric Behçet's disease: an in silico proof-of-concept study.

Behçet's disease (BD) in childhood is characterised by recurrent inflammatory flares that can result in significant morbidity, most notably with ocula...

AI meets sleep surgery: assessing drug-induced sleep endoscopy interpretation with a large language model.

STUDY OBJECTIVES: To evaluate the performance and safety of a large language model in interpreting drug-induced sleep endoscopy (DISE) videos and prov...

Deep learning-empowered Raman spectroscopy for gastric cancer diagnosis: a review of data pipelines and algorithms.

Raman spectroscopy is emerging as a label-free tool for gastric cancer diagnosis by capturing molecular fingerprints of malignant transformation. Howe...

Interpretable single-cell machine learning identifies PINK1-centred mitophagy as a determinant of metabolic fitness in type 2 diabetes.

BACKGROUND: Progressive pancreatic β-cell dysfunction constitutes a hallmark of type 2 diabetes (T2D), yet the molecular programmes governing metaboli...

Machine learning modeling to predict HCC locations in cirrhotic patients undergoing MRI - a proof-of-concept study.

PURPOSE: Current hepatocellular carcinoma (HCC) surveillance guidelines rely on manually defined LI-RADS (Liver Imaging Reporting and Data System) fea...

Deep learning-based respiratory-triggered magnetic resonance cholangiopancreatography: comparison with conventional respiratory-triggered and breath-hold magnetic resonance cholangiopancreatography.

PURPOSE: To evaluate whether deep learning-based respiratory-triggered (DL) 3D magnetic resonance cholangiopancreatography (MRCP) improves acquisition...

Integration of site-specific deep learning models for automated OAR segmentation in craniospinal proton therapy: a geometric and dosimetric analysis.

PURPOSE: This study evaluated the feasibility of integrating RayStation deep learning auto-segmentation (DLS) models-originally trained for adult head...

The slope of enlightenment: Towards a more realistic understanding of the strengths and weaknesses of artificial intelligence for colorectal polyp assessment.

Artificial intelligence (AI) has rapidly evolved into a transformative adjunct to gastrointestinal (GI) endoscopy, particularly through deep-learning-...

Treatment-specific CT Radiomics Models To Predict Response To Neoadjuvant Therapy And Explore Individualized Treatment Selection In Gastric Cancer.

RATIONALE AND OBJECTIVES: To develop and compare general and treatment-specific radiomics models based on pretreatment computed tomography (CT) for pr...