Artificial Intelligence
Overview
Transforming data into intelligent decisions.
AI in Medical Publishing: Assistance Is Acceptable, Authorship Is Human: BMJ Future Health | August 2026
Introduction: Generative AI is rapidly entering medical writing, research, clinical documentation, and evidence synthesis. The central question is no longer whether researchers will use AI, but where its use is appropriate and where it threatens...
Translating Artificial Intelligence in Pathology into Real Clinical Practice: NEJM AI | August 2026
Introduction: Artificial intelligence (AI) has demonstrated remarkable ability to extract diagnostic, prognostic, and molecular information from routine hematoxylin and eosin (H&E)-stained pathology slides. Despite this rapid scientific progress, adoption of AI in pathology has been...
Machine Learning Outperforms AIMS65 and Glasgow-Blatchford Score for Mortality Prediction in GI Bleeding: Adv GastroHep | August 2026
Gastrointestinal bleeding (GIB) is a common medical emergency associated with significant morbidity and mortality. Early identification of high-risk patients is critical for decisions regarding hospitalization, intensive care, urgent endoscopy, and resource allocation. Although the AIMS65...
AI-Powered Histology Predicts the Best Adjuvant Chemotherapy for Resected Pancreatic Cancer (PANCprAId): JCO | August 2026
Introduction: Following curative surgery for pancreatic ductal adenocarcinoma (PDAC), modified FOLFIRINOX (mFOLFIRINOX) has become the preferred adjuvant chemotherapy for fit patients, while gemcitabine (GEM) remains an option for those unable to tolerate intensive treatment. However,...
AI-Triggered Rapid Response Teams: NEJM AI | July 2026
Introduction: Early recognition of clinical deterioration remains a major challenge in hospitalized patients. This multicenter study evaluated whether combining the Epic Deterioration Index (EDI), a real-time machine-learning prediction model, with automatic Rapid Response Team (RRT)...
Machine Learning Outperforms Conventional Noninvasive Tests for MASH Fibrosis Detection: CGH | July 2026
Introduction: Identifying patients with MASH and significant fibrosis (F2–F3) is critical because this group is eligible for emerging FDA-approved therapies. This study compared 28 existing noninvasive tests (NITs) with newly developed machine-learning (ML) models for...
AI-Empowered Human Microbiome Research: Gut | July 2026
Introduction: Advances in high-throughput sequencing have generated enormous microbiome datasets, but extracting meaningful biological insights remains challenging. This review highlights how artificial intelligence (AI) is transforming microbiome research by enabling deeper understanding of host–microbe interactions...
AI and U.S. Healthcare Costs: NEJM Catalyst | July 2026
Introduction: Artificial intelligence is rapidly transforming healthcare through drug discovery, clinical decision support, remote monitoring, and administrative automation. While AI is widely expected to reduce healthcare costs, this perspective argues that current payment models and...
Large AI Models and Healthcare: Nature Medicine | June 2026
Introduction: Large frontier AI models such as GPT-5 and Gemini have achieved impressive results across numerous healthcare benchmarks. However, high benchmark scores alone may not reflect real-world clinical reliability. This study systematically evaluated the robustness...
AI Ethics From Silicon Valley to the Vatican: JAMA | July 2026
Introduction: Artificial intelligence is rapidly transforming medicine, but its influence extends far beyond healthcare. This JAMA AI Conversations article explores how AI ethics has become a global societal issue, engaging technology leaders, policymakers, healthcare professionals,...
Physician-Complementing AI in Oncology: The ASCO Post | June 2026
Introduction: Artificial intelligence is rapidly transforming oncology, evolving from image interpretation and pathology analysis to supporting complex clinical decision-making. This perspective argues that AI should enhance the capabilities of oncologists rather than replace their expertise....
AI-Based Clinical Trial End Points: A New Era in Drug Development: NEJM AI | July 2026
Introduction Clinical trial endpoints have traditionally relied on expert human interpretation, particularly for pathology-based outcomes. However, variability between observers, cost, and time remain important limitations. This NEJM AI perspective discusses how artificial intelligence is beginning...
Medical AI Assistant: Publication or Medical Device?: NEJM AI | July 2026
Introduction: As artificial intelligence becomes increasingly integrated into clinical practice, an important question arises: should AI assistants be regulated as medical devices or viewed as evidence-based clinical methodologies? This NEJM AI perspective proposes a new...
LLMs Rapidly Transform European Gastroenterology Practice : Gut | June 2026
Introduction: Large language models (LLMs), exemplified by ChatGPT and similar artificial intelligence platforms, are rapidly reshaping healthcare delivery, medical education, and scientific research. In gastroenterology, these technologies have the potential to support clinical decision-making, streamline...
Competing Risk Analysis in HCT & IEC Research : Transplant Cell Ther | May 2026
Introduction Competing risks are frequently encountered in hematopoietic cell transplantation (HCT) and immune effector cell (IEC) therapy research, particularly when mutually exclusive outcomes coexist. A classical example is treatment-related mortality (TRM) competing with disease relapse,...
AI Meets Endoscopy for early ESCC Detection: Endoscopy | April 2026
Introduction Esophageal squamous cell carcinoma is a highly aggressive malignancy where early detection and accurate assessment of invasion depth are critical for determining optimal treatment strategies. Current tools such as magnifying endoscopy and endoscopic ultrasonography...
AI-Based Risk Prediction for HCC: Cancer Discovery | April 2026
Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally, largely due to late diagnosis and inadequate risk stratification. Current clinical risk scores have limited predictive accuracy and often fail to identify high-risk individuals...
AI for Endoscopic and Histologic Assessment in IBD Trials: J Crohn’s and Colitis, March 2026
Introduction Accurate assessment of disease activity in inflammatory bowel disease (IBD) clinical trials relies on central reading of endoscopic and histologic images. Although considered the current gold standard, this process is limited by interobserver variability,...
AI-Enabled Imaging for Predicting Postoperative Recurrence in Crohn’s Disease: Gut, March 2026
Postoperative recurrence (POR) remains a major challenge in Crohn’s disease (CD), occurring in up to 70% of patients within the first year after intestinal resection. Current surveillance strategies—primarily ileocolonoscopy and fecal calprotectin—have limited accuracy and...
ACG Delphi Consensus on AI in GI- AMJ Feb.26
This American College of Gastroenterology (ACG)–led modified Delphi consensus provides a comprehensive framework for responsible integration of artificial intelligence (AI) into gastroenterology, hepatology, and endoscopy practice. A multidisciplinary task force of 32 experts and 12...
Cholangioscopy in biliary tract disease(Endoscopy, Jan-2026)
Cholangioscopy is an advanced endoscopic procedure that allows direct visualization of the bile ducts and is commonly used to evaluate biliary tract diseases, including strictures, stones, and malignancies. It is particularly valuable in cases where...
Cholangioscopy in biliary tract disease (Thieme, Jan-2026)
Cholangioscopy is a specialized endoscopic procedure that allows direct visualization of the bile ducts, aiding in the diagnosis and management of biliary tract diseases such as strictures, stones, and malignancies. It is particularly valuable when...
Laboratory-Based ML Model for Predicting Advanced Fibrosis in MASLD
The study described focuses on the development of a laboratory-based machine learning (ML) model aimed at predicting advanced liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Below is a detailed explanation of...
Trial of Artificial Intelligence and Adjunctive Polyp Detection - J of JGH - Jan,26
The study referenced, titled "Trial of Artificial Intelligence and Adjunctive Polyp Detection," published in the Journal of Gastroenterology and Hepatology (JGH) on January 26, evaluates the effectiveness of artificial intelligence (AI)-assisted polyp detection systems in...
Perihilar Cholangiocarcinoma and Deep Learning
Perihilar Cholangiocarcinoma (pCCA) and Deep Learning What is Perihilar Cholangiocarcinoma (pCCA)? Perihilar cholangiocarcinoma (pCCA) is a type of bile duct cancer that arises near the liver's hilum, where the bile ducts exit the liver. It...
Multicenter Validation of an AI-Based Cholangioscopy System for Biliary Disease Evaluation
The multicenter validation study focused on assessing the performance of an artificial intelligence (AI)-based system for evaluating biliary tract disease using cholangioscopy video footage. Accurate differentiation between benign and malignant biliary strictures is a major...
AI algorithm for early identification of MASLD
The AI algorithm developed for the early identification of Metabolic Dysfunction–Associated Steatotic Liver Disease (MASLD) is a significant advancement in leveraging artificial intelligence and natural language processing (NLP) technologies to detect this prevalent yet often...
GEMA-AI: Gender-Equity Model for Liver Transplant Waiting List Prioritization
GEMA-AI, or the Gender-Equity Model for Liver Transplant Waiting List Prioritization, is an innovative artificial intelligence-based model designed to improve fairness and accuracy in liver transplant allocation, with a specific focus on addressing gender disparities....
AI-assisted versus conventional reading in pan-intestinal capsule endoscopy
The comparison between AI-assisted pan-intestinal capsule endoscopy (AI-PCE) and conventional reading of pan-intestinal capsule endoscopy (CR-PCE) highlights significant advancements in the diagnostic capabilities of AI technology in the context of suspected mid-lower gastrointestinal bleeding (MLGIB)....
Machine learning in GI Endoscopy
Machine learning (ML) has revolutionized gastrointestinal (GI) endoscopy by improving diagnostic precision, reducing variability, and streamlining workflows. ML algorithms analyze endoscopic images or videos to detect patterns, identify lesions, and assist in diagnosing, characterizing, or...
Submucosal vessel detection during third-space endoscopy - Role of AI
The role of artificial intelligence (AI) in submucosal vessel detection during third-space endoscopy, such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM), has been explored to enhance procedural safety and efficiency. These advanced...
CADe colonoscopy in colorectal cancer screening - ESGE Postional Statement
The ESGE (European Society of Gastrointestinal Endoscopy) Position Statement on computer-assisted detection (CADe) in colonoscopy for colorectal cancer (CRC) screening and post-polyp surveillance provides a cautious but favorable recommendation for its use. Here is a...
AI for submucosal vessel detection during third-space endoscopy
Submucosal vessel detection is critically important in third-space endoscopy because this advanced procedure involves creating a pathway within the layers of the gastrointestinal wall to treat conditions like achalasia, tumors, or gastrointestinal leaks. During this...
Cancer Recurrence in Patients With CRC - Role of AI
The role of AI in detecting and analyzing cancer recurrence in patients with colorectal cancer (CRC) has been transformative, particularly with advancements like the DFCI-imaging-student model. Below is a detailed explanation of how AI contributes...
AI ML-based nomogram for mortality risk stratification in cirrhotic patients
The AI/ML-based nomogram developed in this study serves as a predictive tool for estimating in-hospital mortality risk among cirrhotic patients with sepsis. This retrospective single-center study analyzed data from 264 patients admitted between January 2018...
Deep learning : Predicting HCC surgery success with multimodal imaging
Deep learning is a subset of machine learning that uses artificial neural networks to model and analyze complex data patterns. It is particularly effective in tasks involving large datasets, such as image recognition, natural language...
Artificial intelligence-assisted colonoscopy improves adenoma detection rates
Yes, artificial intelligence (AI)-assisted colonoscopy has been shown to improve adenoma detection rates (ADR). Adenoma detection is a critical measure in colonoscopy, as higher ADRs are directly linked to reduced colorectal cancer (CRC) incidence and...
Esophageal Squamous Cell Carcinoma, Post operative Recurrence and Machine Learning
Esophageal squamous cell carcinoma (ESCC) is a common and aggressive form of cancer, with a high risk of postoperative recurrence. Accurate prediction of recurrence is essential for optimizing follow-up care and tailoring treatment strategies to...
Machine learning (ML) in GI Endoscopy
Machine learning (ML) in gastrointestinal (GI) endoscopy is revolutionizing the field by improving diagnostic precision, reducing human variability, and enhancing workflow efficiency. Here is a detailed explanation of the role, applications, challenges, and future outlook...
AI in health and health care: summary of the JAMA Summit
The JAMA Summit on AI in health and health care highlighted the transformative impact of artificial intelligence on the field, emphasizing its potential to improve access, quality, and affordability of care while addressing its associated...
Integrating Machine Learning and Bioinformatics to Develop a Gene-Based Prognostic Model for Gastric Cancer
Integrating machine learning and bioinformatics has proven to be a transformative approach in developing a gene-based prognostic model for gastric cancer. This study utilized transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and...
Artificial neural network-predicted PPG and HVPG with measured PPG in decompensated cirrhosis
The study compared the performance of artificial neural network (ANN)-predicted portal pressure gradient (PPG) and hepatic venous pressure gradient (HVPG) with measured PPG in patients with decompensated cirrhosis. Here's a detailed breakdown of the findings:...
Evaluation of NLP Algorithms for Identifying IBD from Clinical Records
The study conducted a comprehensive evaluation of 15 natural language processing (NLP) algorithms to identify patients with inflammatory bowel disease (IBD) from free-text secondary care clinical records. Here are the detailed findings related to the...
Large language models (LLMs) with script concordance testing (SCT)
The benchmarking study that evaluated the clinical reasoning performance of large language models (LLMs) using Script Concordance Testing (SCT) provided several insights into their capabilities and limitations in medical applications. Below is a detailed summary...
TRIALSCOPE
TRIALSCOPE is a cutting-edge framework designed for clinical trial simulation using real-world data (RWD). It leverages advanced artificial intelligence (AI) and causal inference techniques to extract, clean, and analyze patient data sourced from electronic medical...
CADe and CRC
Computer-Aided Detection (CADe) and Colorectal Cancer (CRC) are connected through the use of artificial intelligence (AI) technologies to improve the detection of polyps during colonoscopy procedures, which is a critical step in CRC prevention. Here's...
AI-Based Computer-Aided Detection in Colonoscopy
AI-based computer-aided detection (CADe) in colonoscopy refers to the use of artificial intelligence (AI) systems to assist healthcare professionals in identifying abnormalities, such as polyps or adenomas, in the colon during a colonoscopy procedure. These...
More topics
Small and Large Bowel
Evidence-driven progress in bowel health management.
Exam Corner
Your hub for focused learning and smart preparation.
Cirrhosis Liver
Precision insights for better liver outcomes.
Liver Transplantation
Advancing outcomes through surgical excellence.
Fatty Liver Disease
Promoting liver health through early insight and action.
Endoscopy
Clear vision for a healthier tomorrow.
Basic Sciences
Building the foundation of medical understanding.
HCC
Awareness saves lives. Early action matters.
IBD
Evidence-based care for chronic intestinal conditions.
Hepatitis
Evidence-based insights. Better liver health
Oncology
Transforming Oncology with Next-Gen Science
Gallbladder and Pancreas
Precision insights. Smarter healthcare.
Upper GI Tract
Supporting better digestion through informed care.”
GI Surgery
Advancing precision and outcomes in gastrointestinal care.
References
We are pioneers in clinical intelligence, dedicated to helping gastroenterologists harness the power of artificial intelligence to drive precision, efficiency, and patient growth.