Wallace, Michael B

Abstract: Background Artificial intelligence (AI)-assisted colonoscopy reduces costs in colorectal cancer screening, but its incremental value relative to non-AI resect-and-discard (RD) strategies for diminutive polyps (≤ 5 mm) remain unclear. We aimed to compare the clinical, economic, and environmental outcomes of AI-assisted and non-AI RD strategies with standard of care (SoC). Methods In our Markov model simulating 6 million individuals aged ≥45 years undergoing screening colonoscopy, 4 strategies were evaluated: SoC, non-AI RD, CADe-assisted RD (CADe-RD), and CADe+CADx-assisted RD (CADe+CADx-RD). The primary outcome was total quality-adjusted life years (QALYs), with secondary outcomes including cost and carbon dioxide (CO₂) emissions. Results Non-AI RD achieved the lowest total cost ($36.6B) and carbon emissions (0.90 million kg CO₂), while yielding the highest QALYs (51.37 million), although differences in QALYs across strategies were small (<0.01%). CADe-RD and CADe+CADx-RD remained cost-saving relative to SoC ($36.9B and $37.4B vs $37.8B, respectively) but were associated with higher costs than non-AI RD. CADe modestly increased adenoma detection without improving QALYs, while the addition of CADx increased costs and emissions and resulted in slightly lower QALYs. Sensitivity analysis showed that CADe+CADx-RD matched or exceeded CADe-RD QALYs only within a limited region of high diagnostic performance. At currently reported CADx performance levels, CADe+CADx-RD yielded lower QALYs and higher costs than CADe-RD. CADe+CADx-RD achieved non-inferiority in QALYs relative to CADe-RD only within a limited region of high diagnostic performance. Conclusions In our non-deterministic model, non-AI RD provided the optimal balance of clinical benefit, cost savings, and lowest environmental impact.

Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy into a more standardized, data-driven, and workflow-integrated field. Advances in computer-assisted detection (CADe), diagnosis (CADx), quality assessment (CAQ), natural language processing (NLP), and multimodal deep learning have expanded AI applications across colonoscopy, upper endoscopy, endoscopic ultrasound (EUS), ERCP, cholangioscopy, and capsule endoscopy. These systems have demonstrated improvements in lesion detection, procedural quality assessment, workflow efficiency, and diagnostic support. However, current evidence remains largely focused on surrogate outcomes rather than patient-centered clinical benefits, while challenges related to generalizability, explainability, regulatory oversight, automation bias, and workflow integration continue to limit widespread adoption. Future progress will depend on prospective real-world validation, diverse datasets, explainable AI frameworks, and careful integration of human–AI interaction into clinical practice. Overall, AI is evolving from a supportive adjunct into an increasingly integrated component of gastrointestinal endoscopy with the potential to improve procedural quality, diagnostic consistency, and clinical efficiency.

INTRODUCTION: Colonoscopies reduce colorectal cancer incidence and mortality, but challenges remain in detecting all precancerous lesions. Operator fatigue, technique variability, and subtle lesions can lead to missed adenomas, highlighting the need for tools that standardize detection quality. Computer-aided detection (CADe) systems use artificial intelligence to enhance lesion detection and improve screening colonoscopy quality. This study evaluates the impact of CADe on key quality and efficiency metrics in a clinical setting. METHODS: A single-center retrospective study at an academic tertiary center analyzed 4,028 colonoscopies from October 2022 to December 2023. The CADe system was used in 4 of 8 endoscopy suites, creating a CADe and control group. Propensity matching accounted for age, sex, indication, and physician. Primary outcomes included adenoma detection rate (ADR) and polyp detection rate (PDR). Adenomas per colonoscopy (APC) and polyps per colonoscopy (PPC) were also measured. Per-polyp analyses were performed as a tertiary outcome. RESULTS: ADR was significantly higher in the CADe group (38.6%) vs control (34.2%) (rate ratio [RR] = 1.127, P < 0.05). PDR was significantly higher in the CADe group (67.2%) vs control (59.4%) (RR = 1.130, P < 0.05). APC was significantly higher in the CADe group (M = 0.406, SD 0.637) vs control (M = 0.359, SD 0.599) (RR = 1.131, P < 0.05). PPC was significantly higher in the CADe group (M = 1.297, SD 1.188) vs control (M = 1.046, SD 1.002) (RR = 1.240, P < 0.05). The increase in polyp resection with CADe was driven predominantly by ≤5 mm lesions, with comparatively smaller differences for larger polyps. DISCUSSION: Implementing CADe in clinical practice significantly improved ADR, PDR, APC, and PPC. However, the increase in ADR seems inflated by the detection of polyps ≤5 mm, whose clinical significance in reducing colorectal cancer remains uncertain.

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps.

Abstract Endoscopic mucosal resection (EMR) techniques for large (≥20 mm) nonpedunculated colorectal polyps (LNPCPs) have expanded with the introduction of ablation and cold EMR. This study assessed adverse events (AEs) for the newer EMR techniques, including cold EMR, compared with hot EMR. We conducted a secondary analysis of four prospective multicenter studies of consecutive patients with LNPCPs undergoing EMR from 2019 to 2024. The primary outcome was serious AEs (SAEs) with cold and hot EMR. Secondary outcomes included SAEs in the hot EMR subgroups (no ablation [hEMR], margin ablation [hEMR-m], margin and base ablation [hEMR-mb]). 1762 patients (mean age 65.8; 1890 LNPCPs) were included: 522 cold and 1368 hot EMRs (368 hEMR, 770 hEMR-m, 230 hEMR-mb). SAEs were higher with hot EMR (4.7%, 95%CI 3.6%–5.9%) vs. cold EMR (1.9%, 95%CI 0.9%–3.5%), also for the subgroups of hEMR (6.0%, 95%CI 3.8%–8.9%), hEMR-m (3.9%, 95%CI 2.6%–5.5%), and hEMR-mb (5.2%, 95%CI 2.7%–8.9%). Serious postendoscopic bleeding (PEB) was numerically higher with hot EMR (2.3%, 95%CI 1.6%–3.3%) vs. cold EMR (1.3%, 95%CI 0.5%–2.7%), also for the subgroups of hEMR (3.0%, 95%CI 1.5%–5.3%), hEMR-m (1.9%, 95%CI 1.1%–3.2%), and hEMR-mb (2.6%, 95%CI 1.0%–5.6%). Perforation, intraprocedural and post-procedural, was numerically higher with hot EMR (1.2%, 95%CI 0.7%–2.0%) vs. cold EMR (0.2%, 95%CI 0.0%–1.1%). hEMR-m and hEMR-mb with clipping had lower rates of serious and overall PEB than no clipping. Cold EMR demonstrated lower rates of SAEs, serious PEB, and perforation compared with hot EMR. Perforation and mortality occurred almost exclusively after hot EMR. Hot EMR with margin +/− base ablation did not increase SAEs compared with hot EMR without ablation.

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.

Background Gastrointestinal cancers comprise nearly one-third of global mortality from cancer, yet the comprehensive global burden of these cancers remains uninvestigated. Objective We aimed to assess the global, regional and national burden of gastrointestinal cancers. Designs Data on oesophagus, gastric, colorectal, liver, pancreas and biliary tract cancers were extracted from the Global Burden of Disease 2021 database. Age-standardised incidence rate (ASIR) and age-standardised death rate (ASDR) were calculated by sex, region and Sociodemographic Index (SDI). Results In 2021, there were 5.26 million incidences and 3.70 million deaths from gastrointestinal cancer. The greatest burden is from colorectal, followed by gastric, oesophageal, pancreatic, liver and biliary tract cancer. We noted geographical and socioeconomic differences in ASIR and ASDR across all types of cancers. From 2000 to 2021, ASIR increased for colorectal cancer (annual percent change (APC): 0.10%, 95% CI 0.05% to 0.14%), pancreatic cancer (APC: 0.27%, 95% CI 0.14% to 0.41%), and liver cancer from metabolic dysfunction-associated steatotic liver disease (APC: 0.62%, 95% CI 0.58% to 0.67%) and alcohol-related liver disease (APC: 0.26%, 95% CI 0.22% to 0.30%). ASDR increased for pancreatic cancer (APC: 0.18%, 95% CI 0.02% to 0.34%). Higher SDI countries had higher incidence rates for most types of gastrointestinal cancer. Conclusions Although the ASIR of oesophageal, gastric and biliary tract cancer has decreased, the ASIR still increased in colorectal, pancreatic and liver cancer from steatotic liver disease. Public policies are important for controlling gastrointestinal cancers—most importantly, reducing alcohol consumption, hepatitis B immunisation and tackling the burden of metabolic diseases.

Background Piecemeal endoscopic mucosal resection (EMR) is an acceptable technique for T1a esophageal adenocarcinoma, but en bloc R0 excision is advocated for T1b disease as it may offer a potential cure and mitigate recurrence. Thus, distinguishing between T1a and T1b disease is imperative under current treatment paradigms. We investigated whether expert Barrett’s endoscopists could make this distinction based on optical evaluation. Methods Endoscopic images of histologically confirmed high grade dysplasia (HGD), T1a, and T1b disease (20 sets for each) were compiled from consecutive patients at a single institution. Each set contained four images including an overview, a close-up in high definition white light, a near-focus magnification image, and a narrow-band image. Experts predicted the histology for each set. Results 19 experts from 8 countries (Australia, USA, Italy, Netherlands, Germany, Canada, Belgium, and Portugal) participated. The majority had been practicing for > 20 years, with a median (interquartile range) annual case volume of 50 (18–75) for Barrett’s EMR and 25 (10–45) for Barrett’s endoscopic submucosal dissection. Esophageal adenocarcinoma (T1a/b) could be distinguished from HGD with a pooled sensitivity of 89.1 % (95 %CI 84.7–93.4). T1b adenocarcinoma could be predicted with a pooled sensitivity of 43.8 % (95 %CI 29.9–57.7). Fleiss’ kappa was 0.421 (95 %CI 0.399–0.442; P < 0.001), indicating fair-to-moderate agreement. Conclusions Expert Barrett’s endoscopists could reliably differentiate T1a/T1b esophageal adenocarcinoma from HGD. Despite fair-to-moderate agreement for T staging, T1b disease could not be reliably distinguished from T1a disease. This may impact clinical decision making and selection of endoscopic techniques.

The incidence of early-onset colorectal cancer (EO-CRC) in individuals under 50 years old is rapidly increasing in the United States. This study aims to evaluate EO-CRC incidence rates using data from the Global Burden of Disease Study (GBD) 2021, providing insights into trends from 1990 to 2021. We employed an age–period–cohort (APC) model analysis to estimate the effects of age, time period, and birth cohort on EO-CRC incidence. Our findings indicate that the number of EO-CRC cases rose from 6256 (95% UI: 6059–6456) in 1990 to 9311 (95% UI: 8859–9744) in 2021, a 49% increase from 1990 to 2021. The age-standardized incidence rate per 100,000 population increased by 34% during this period. The net drift in females (0.22%, 95% CI: 0.20–0.24) was slightly higher than in males (0.21%, 95% CI: 0.19–0.23) (p = 0.45). The APC analysis revealed that being over 25 years old, the period from 2005–2021, and being born after 1983 negatively impacted EO-CRC incidence rates, with a sharp rise after 2000 and a reduction among females from 2017 to 2021. Our study highlights the need for targeted prevention strategies and further research to understand these trends.

Background: While bariatric surgery may reduce obesity-associated inflammation, alterations in gut microbiome and nutrition could impact inflammatory bowel disease (IBD) risk. This study aimed to investigate the association between bariatric surgery and new onset IBD. Methods: A systematic review and meta-analysis of observational studies was conducted from inception to 31 January 2024. Risk estimates were pooled using a DerSimonian and Laird random-effects model, and adjusted hazards ratios (HRs) with corresponding 95% confidence interval (CI) were reported. The modified Newcastle-Ottawa Quality Assessment Scale (NOS) was used to examine the risk of bias. Results: Of 98 articles screened, four studies comprising 4,727,600 participants were included in the systematic review and two studies in the meta-analysis. Included studies had high quality and low risk of bias according to NOS. The pooled analysis revealed a significant risk of new onset IBD (HR: 1.28, 95% CI: 1.04–1.53, I2 = 74.9%), particularly Crohn’s disease (HR: 1.75, 1.59–1.92, I2 = 0), following bariatric surgery, but no significant risk of ulcerative colitis (HR: 0.93, 0.75–1.11, I2 = 11.5%). Conclusions: This meta-analysis found that bariatric surgery was associated with a higher risk of developing Crohn’s disease. Patients should be counseled on IBD risk pre-surgery, and symptomatic patients should be evaluated post-surgery to enable early diagnosis and management.