SEARCH · Search PubMed

Results for “Artificial pancreas”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

126 recordsLinked to original sources

PubMed2026

Glycemic and safety outcomes of the insulin-only bionic pancreas in older adults and individuals with impaired awareness of Hypoglycemia: a post hoc analysis of a randomized pivotal trial.

AIMS: Evaluate the efficacy and safety of iLet Bionic Pancreas (BP) in older adults and individuals with impaired awareness of hypoglycemia (IAH). METHODS: This post hoc analysis used individual participant-level data from the Insulin-Only Bionic Pancreas Pivotal Trial (n = 440; NCT04200313). Eligible participants (n = 96) with type 1 diabetes, aged ≥ 60 years and/or had IAH (Clarke score ≥ 4), were randomized to BP with aspart/lispro (BP-Asp/Lis; n = 45), BP with fast-acting aspart configuration (BP-Fiasp; n = 31), or standard care (SC; n = 20) for 13 weeks. RESULTS: Compared with SC, time-in-range (70-180 mg/dL) significantly increased by 7.49 % (95 % CI: 2.61 to 12.38; ∼1.8 h/day) with BP-Asp/Lis and by 8.28 % (95 % CI: 3.15 to 13.41; ∼2.0 h/day) with BP-Fiasp, driven by reduced hyperglycemia. No significant differences were observed in hypoglycemia exposure. Severe hypoglycemia occurred in four participants (four events) on BP-Asp/Lis and one participant (two events) on SC. One diabetic ketoacidosis event occurred on BP-Fiasp due to an infusion set failure. CONCLUSIONS: In high-risk, clinically vulnerable populations, the BP system significantly improved glycemic control while maintaining safety parity with respect to hypoglycemia risk, providing a resilient therapeutic alternative for vulnerable cohorts.

Humans↗

PubMed2026

Development and Crossover Evaluation of an Artificial Intelligence-Assisted System for Solid Pancreatic Lesion Detection and Pancreatic Parenchyma Recognition in Endoscopic Ultrasonography (With Video).

BACKGROUND AND STUDY AIMS: Pancreatobiliary endoscopic ultrasonography (EUS) is technically demanding, and supervised training opportunities are limited. We developed an artificial intelligence (AI) overlay system for detecting solid pancreatic lesions (SPL) and recognizing pancreatic parenchyma (PP) and evaluated its effect on reader performance. PATIENTS AND METHODS: Across six centers, two deep learning-based models were trained using expert-annotated EUS frames. We then conducted a randomized, two-sequence, two-period crossover reader study in which eight endosonographers (five novices and three experts) interpreted image sets with and without AI assistance. The primary endpoint was superiority of sensitivity for SPL detection among novices; key secondary endpoints included specificity and PP recognition. RESULTS: From 118 patients, 120 SPL-positive/negative image sets and 160 PP-positive/negative image sets were constructed. Among novices, AI assistance improved SPL detection sensitivity (88.7% vs. 76.8%, p < 0.001) and accuracy (86.4% vs. 78.7%), while specificity met the predefined noninferiority criterion (84.2% vs. 80.5%, p < 0.001). For PP recognition, sensitivity increased numerically (86.3% vs. 83.3%) but did not meet the predefined superiority criterion (p = 0.095); specificity met the noninferiority criterion (87.8% vs. 81.0%), and accuracy increased from 82.1% to 87.0%. Among experts, sensitivity was maintained for both tasks, whereas specificity increased with AI assistance. CONCLUSIONS: AI assistance improved SPL detection among novice endosonographers. For PP recognition, sensitivity increased without reaching statistical superiority, whereas specificity met the predefined noninferiority criterion. These findings support a potential adjunctive role for AI in EUS interpretation.

Humans↗

PubMed2026

Closed-loop insulin delivery for glycaemic control in hospitalised and perioperative adults: A systematic review and meta-analysis of randomised controlled trials.

We evaluated whether closed-loop insulin delivery improves glycaemic control in hospitalised and perioperative adults. PubMed/MEDLINE, Embase, CENTRAL, and ClinicalTrials.gov were searched from inception to 29 June 2026 for randomised controlled trials comparing closed-loop or automated insulin delivery with usual care or conventional insulin therapy. Random-effects meta-analyses were conducted; risk of bias was assessed using RoB 2 and certainty of evidence using GRADE. Seven trials involving 375 analysed participants were included. Closed-loop insulin delivery increased time in target glucose range by 23.91 percentage points (95% CI 19.40 to 28.43; I2 = 0%) and reduced mean glucose by 1.79 mmol/L (95% CI 1.06 to 2.53 lower; I2 = 36.3%); certainty was moderate for both outcomes. Two trials involving 69 participants reported compatible participant-level data for clinically significant hyperglycaemia, and both estimates favoured closed-loop insulin delivery, although the evidence was exploratory and imprecise. No severe hypoglycaemic events occurred in either group, precluding reliable estimation of comparative safety. Closed-loop insulin delivery may improve glycaemic process measures, but larger pragmatic trials are needed to establish clinical benefits, safety, and implementation feasibility.

Humans↗

PubMed2026

Protein sorting and proteostasis mechanisms in CFTR-related exocrine pancreas dysfunction: A systematic narrative review.

The pancreas consists of exocrine and endocrine compartments. In the exocrine pancreas, cystic fibrosis transmembrane conductance regulator (CFTR) functions mainly in ductal epithelial cells as a chloride and bicarbonate channel. Its activity depends on proper protein folding, trafficking, and localization to the apical membrane. This systematic narrative review aims to synthesize the available evidence on the role of protein sorting machinery in CFTR channelopathies and its contribution to exocrine pancreatic dysfunction. A thorough search was conducted using PRISMA criteria on PubMed, Wiley Online Library, and Scopus for studies published in English between January 2000 and November 2025. Twenty studies that met the inclusion criteria were included in this review. Pathogenic CFTR variants impair protein folding, endoplasmic reticulum (ER) exit, and endosomal recycling, resulting in reduced apical membrane expression and stability. These defects disrupt the localization of associated transporters and secretory proteins, impair ductal bicarbonate secretion, alter zymogen handling, and promote acinar injury, although these claims are supported mainly by indirect experimental models and therefore require clinical confirmation. CFTR channelopathies in the exocrine pancreas encompass both ion transport defects and broader disruptions of protein sorting machinery. CFTR may contribute to the assembly, stabilization, or localization of selected apical transport complexes, and its loss can secondarily alter epithelial organization. Therapeutic approaches targeting both channel correction and intracellular trafficking may improve pancreatic function and mitigate disease progression.

Humans↗

PubMed2026

Pancreatic fat and the risk of future dysglycemia: a systematic review and meta-analysis.

CONTEXT: Pancreatic fat has emerged as a metabolically relevant ectopic fat depot, but its longitudinal association with future dysglycemic outcomes remains incompletely defined. OBJECTIVE: This study aims to evaluate the longitudinal association of pancreatic fat with incident type 2 diabetes (T2D) and glycemic progression, with exploratory narrative synthesis of evidence in lean populations. DATA SOURCES: PubMed, Embase, Web of Science, and the Cochrane Library were searched from inception to March 1, 2026. STUDY SELECTION: Longitudinal observational studies assessing pancreatic fat at baseline and reporting subsequent incident T2D, glycemic progression, or both were included. DATA EXTRACTION: Two reviewers independently screened studies and extracted data. Methodological quality was assessed using the Newcastle-Ottawa Scale. DATA SYNTHESIS: Ten studies were included. In the primary binary meta-analysis (5 studies), higher pancreatic fat burden was associated with incident T2D (pooled effect estimate, 2.56; 95% CI, 1.27-5.14; I2 = 93.3%). Exclusion of 1 influential ultrasound-based study attenuated heterogeneity while preserving the association (pooled effect estimate, 1.45; 95% CI, 1.23-1.73; I2 = 10.2%). A separate continuous analysis (3 studies) also supported an association with incident T2D (1.17; 95% CI, 1.04-1.32; I2 = 79.2%), although exposure scales were not directly comparable. Pancreatic fat was also associated with glycemic progression (2 studies; 1.96; 95% CI, 1.10-3.48; I2 = 73.2%). Exploratory lean-population evidence was limited to 2 analytical contexts, synthesized narratively, and directionally consistent with the overall findings. CONCLUSION: Pancreatic fat was associated with adverse future glycemic outcomes across multiple analytical contexts. These findings support pancreatic fat as a potentially meaningful imaging-derived marker of future dysglycemia, while suggesting that heterogeneity is partly explained by differences in exposure ascertainment.

Humans↗

PubMed2025

The molecular mechanism of cuproptosis and research progress in pancreatic diseases.

PURPOSE: Cuproptosis has been proven to be a novel mode of cell death, distinct from other types of cell death such as necrosis, ferroptosis, pyroptosis, and apoptosis. This study aims to systematically review the molecular mechanisms of cuproptosis in recent years and its research progress in pancreatic diseases. METHODS: By searching PubMed and Web of Science databases, 113 key literatures were included for thematic analysis, covering the molecular mechanism of cuproptosis and its role in the occurrence and development of pancreatic cancer, acute and chronic pancreatitis, diabetes, pancreatic cyst, pancreatic injury and pancreatic neuroendocrine tumor. RESULTS: Cuproptosis refers to the accumulation of copper ions in cells, which leads to instability of ferritin and aggregation of acylated proteins, resulting in oxidative stress-related cell death. Recent studies have shown that cuproptosis plays an important role in the occurrence and development of various pancreatic diseases, such as pancreatic cancer, acute and chronic pancreatitis, diabetes, pancreatic cysts, pancreatic injuries and pancreatic neuroendocrine tumor. The inducers of cuproptosis, such as disulfiram, chloroquinolones, and perilla phenols, alleviate pancreatic cancer by promoting cell cuproptosis. Copper chelators such as tetraethylenepentamine and tetrathiomolybdate promote the recovery of pancreatic injury by inhibiting cell cuproptosis. CONCLUSIONS: Cuproptosis plays a crucial role in the pathogenesis of pancreatic diseases. Further research on the cuproptosis pathway may become a potential target for the treatment of pancreatic diseases.

Animals↗

PubMed2026

Early Analgesia for the Management of Acute Pancreatitis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: We aimed to evaluate the efficacy and safety of early analgesic interventions, particularly NSAIDs versus opioids, in reducing pain and improving clinical outcomes among adults with AP. METHODS: A systematic literature search was conducted across PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Scopus, and ClinicalTrials.gov from database/registry inception to December 2025 to obtain relevant data. Randomized controlled trials involving adults aged 18 years or older diagnosed with AP, irrespective of the etiology and severity, who were administered analgesics (opioids, nonsteroidal anti-inflammatory drugs, cyclooxygenase-2 inhibitors, epidural anesthesia, local anesthesia, and paracetamol) and compared with placebo, conventional treatment, or another analgesic modality were included in this review. The primary outcome assessed was pain reduction. The secondary outcomes assessed were the need for rescue analgesia, length of hospital stay, complications (local and/or systemic), mortality, and adverse drug effects. Risk of bias was assessed using the Cochrane Risk of Bias tool 2.0. Effect estimates were pooled using a random-effects meta-analysis (DerSimonian-Laird approach), while nonpooled outcomes were summarized narratively. RESULTS: A total of 13 studies were included in the analysis. NSAIDs provided pain relief comparable to opioids, with a lower incidence of local complications (RR: 0.59, 95% CI: 0.37-0.94). No significant differences in the need for rescue analgesia (OR: 0.88, 95% CI: 0.33-2.35), length of hospital stay (MD: -2.68 d, 95% CI: -6.27 to 0.91), mortality (RR: 0.76, 95% CI: 0.19-3.05), and adverse drug effects (RR: 0.55, 95% CI: 0.17-1.76) were observed. However, the findings are limited by study bias and heterogeneity. CONCLUSION: Early analgesia with NSAIDs has efficacy and safety comparable to opioids in adults with AP, with the advantage of reducing local complications.

Humans↗

PubMed2026

Relationship Between Number of Acute Pancreatitis Episodes and Risk of New-onset Diabetes in the U.S.: A Real-world Data Analysis.

INTRODUCTION: Acute pancreatitis (AP) is a common inflammatory disorder that is associated with increased risk for diabetes mellitus (DM). It remains unclear whether recurrent acute pancreatitis (RAP) is associated with further increased risk of incident DM. This study aims to investigate the association between RAP and incident DM using real-world data. METHODS: We conducted a retrospective cohort study using the MerativeTM MarketScan® claims database (2016-2023), identifying patients with AP and no prior history of DM at baseline. The primary exposure of interest, RAP, was defined as one or more episodes of AP occurring ≥90 days after the index AP diagnosis, whereas one episode of AP referred to a single episode of AP (SAP) with no subsequent recurrence within 90 days following the index event. A multivariable stratified Cox proportional hazards regression models were used to determine the association between RAP and incident DM, identified using ICD-10 codes. RESULTS: In total, 16,184 individuals with AP (mean [SD] age: 45.8 [12.3]) contributed 40,712 person-years of follow-up, during which 1,477 incident cases of DM were documented. Individuals with RAP had an increased risk of incident DM compared with those with a SAP(adjusted HR, 1.92; 95% CI, 1.61-2.29). The risk increased significantly with the frequency of RAP. In comparing the modifying effect of patient demographics and comorbidities, a stronger association between RAP and incident DM was observed in females (adjusted HR, 2.44; 95% CI, 1.87-3.19) than in males (adjusted HR, 1.64; 95% CI, 1.30-2.07; Pinteraction=0.03). Also, stronger associations were observed among younger patients (18-46 y) (adjusted HR=2.56; 95% CI, 1.97-3.31) and among non-tobacco abuse (adjusted HR=2.19; 95% CI, 1.81-2.65), with significant interactions for all comparisons (Pinteraction<0.05. CONCLUSIONS: In this real-world study, RAP was associated with an increased risk of incident DM. Our findings highlight an opportunity for glycemic monitoring and proactive management of patients with RAP to mitigate their risk of developing DM.

AP↗

PubMed2026

Endoscopic Ultrasound-Guided Franseen Fine-Needle Biopsy for Solid Pancreatic Lesions: A Systematic Review and Meta-Analysis.

INTRODUCTION: Accurate tissue acquisition (TA) of solid pancreatic lesions is essential for guiding treatment with endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) being the preferred method. Among FNB designs, the three-pronged Franseen-tip needle demonstrates strong diagnostic performance, though direct head-to-head comparisons with other FNB designs remain limited. METHODOLOGY: This meta-analysis was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420251123856). Eligible studies enrolled patients with solid pancreatic lesions who underwent EUS-guided FNB, directly compared the Franseen-tip with other FNB needles. Six databases were systematically searched through July 2025, and study selection, data extraction, and risk of bias assessment (QUADAS-2 tool) were performed independently by two reviewers. Pooled estimates were generated using random-effects and bivariate hierarchical models. RESULTS: Sixteen studies (2,010 Franseen vs. 2,811 comparator) were included. Bivariate analysis showed that sensitivity and specificity of the Franseen needle were comparable to newer-generation comparator needles (sensitivity 91.3% vs. 94.0%; specificity 99.99% vs. 99.15%), whereas older-generation needles demonstrated lower sensitivity (80.8%) and inferior discriminatory performance (Negative Likelihood Ratio [LR⁻] 0.19 vs. 0.09). Diagnostic accuracy was higher with the Franseen needle (RR 1.07, 95% CI 1.01-1.14; I2 = 69%). Sample adequacy was similar overall (RR 1.04, 95% CI 0.95-1.14) but superior to older-generation needles (RR 1.19, 95% CI 1.02-1.41) and in lesions > 30 mm (RR 1.14, 95% CI 1.02-1.28, I2 = 81.2%). The Franseen needle achieved nominally strong diagnostic performance (DOR 116.6), although small-study effects were observed. Primary procedural outcomes were comparable between Franseen and comparator needles, including technical success (RR 1.00, 95% CI 0.98-1.02) and histological core procurement (RR 1.04, 95% CI 0.92-1.17). The Franseen needle had fewer low-cellularity samples (RR 0.56, 95% CI 0.45-0.69) and lower specimen bloodiness (RR 0.48, 95% CI 0.25-0.90) but a slightly higher overall adverse event rate (RR 1.29, 95% CI 1.06-1.57). CONCLUSION: The Franseen needle provides superior diagnostic accuracy and sample adequacy compared to older-generation FNB needles with comparable performance to newer-generation designs. It reduces low-cellularity samples and specimen bloodiness, although adverse events are slightly increased, with other primary procedural outcomes remaining comparable. TRIAL REGISTRATION: PROSPERO (Registration No. CRD420251123856).

Humans↗

PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

Humans↗

PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗

PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗

PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗

PubMed2026

The future of precision oncology and artificial intelligence in Belgium: scenarios and policy responses.

PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Belgium↗