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PubMed2026

Targeted Nanoparticle Delivery CRISPR/Cas9: overcoming biological barriers, enhancing stability, and improving therapeutic precision.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has emerged as a promising gene-editing platform for genetic disorders; however, its in vivo application remains limited by low delivery efficiency and biological barriers. Many CRISPR payloads fail to reach target sites due to extracellular degradation, immune clearance, and intracellular trafficking limitations. This review examines the interplay between biological barriers and nanoparticle engineering strategies for CRISPR/Cas9 delivery. A barrier-oriented engineering approach is proposed as a central framework, encompassing ligand-based surface modification for enhanced targeting and uptake, improved circulation stability via PEGylation and biomimetic coatings, and optimized payload release through endosomal escape strategies. Stimulus-responsive nanoparticle systems further enable spatiotemporal control over payload release. Nuclear targeting strategies, including optimization of nuclear localization signals (NLS) and exploitation of endogenous trafficking pathways, are highlighted as key factors for improving genome-level editing efficiency. Despite these advances, major challenges-including limited intracellular delivery efficiency, insufficient targeting precision, and safety concerns-continue to hinder clinical translation. Future directions highlight artificial intelligence-driven nanoparticle design, personalized delivery systems, and next-generation CRISPR platforms. Overall, an integrated, barrier-oriented engineering strategy is essential for advancing CRISPR/Cas9 delivery toward clinical applications, ultimately advancing global good health and well-being.

CRISPR/Cas9↗

PubMed2026

Recombinant Human Thrombopoietin Reduces the Need for Platelet Transfusion in Patients With Chronic Liver Disease and Thrombocytopenia.

Chronic liver disease (CLD)-related thrombocytopenia can limit the feasibility of invasive procedures. Recombinant human thrombopoietin (rhTPO) has demonstrated a favorable safety profile without hepatotoxicity. We evaluated the efficacy and safety of rhTPO in patients with CLD-related thrombocytopenia who were undergoing elective invasive procedures. In this multicenter, randomized (2:1), double-blind, placebo-controlled phase III trial, 120 adult Chinese patients with CLD-related thrombocytopenia (platelet count < 50 × 109/L) received rhTPO (n = 80) or placebo (n = 40) once daily for up to 5 or 7 days. The primary endpoint was the proportion of patients with sustained platelet counts ≥ 50 × 109/L from 24 h before invasive procedure to 7 days post-procedure, without requiring emergency bleeding management. The primary endpoint was achieved by 85.0% of patients in the rhTPO group versus 12.5% in the placebo group (p < 0.0001). Preoperatively, platelet counts ≥ 50 × 109/L were achieved in 92.5% and 20.0% of patients in the rhTPO and placebo groups, respectively (p < 0.0001). Platelet transfusion was avoided in 92.5% of rhTPO-treated patients versus 25.0% of placebo-treated patients (p < 0.0001). The median duration of platelet counts ≥ 50 × 109/L was significantly longer with rhTPO than with placebo (21.0 vs. 3.0 days, p = 0.0007). Treatment-related treatment-emergent adverse events (TEAEs) occurred in 12.5% of patients in both the rhTPO and placebo groups. No treatment-related serious adverse events were reported. Overall, rhTPO was effective and well tolerated in patients with CLD-related thrombocytopenia and may represent a viable therapeutic option for those undergoing elective invasive procedures. Trial Registration: www.chinadrugtrials.org.cn: number CTR20230919.

Humans↗

PubMed2026

Efficacy and safety of mitapivat in adults with transfusion-dependent α-thalassaemia or β-thalassaemia (ENERGIZE-T): a double-blind, randomised, multicentre, placebo-controlled, phase 3 trial.

BACKGROUND: The absence of disease-modifying therapies for patients with α-thalassaemia and oral disease-modifying therapies for patients with β-thalassaemia has been a substantial unmet need in these patients. We assessed the efficacy and safety of mitapivat, an oral allosteric activator of pyruvate kinase, in adults with transfusion-dependent thalassaemia. METHODS: ENERGIZE-T is a global, double-blind, randomised, placebo-controlled, phase 3 trial, conducted across 19 countries in North America, Europe, Asia-Pacific, South America, and the Middle East. Patients aged 18 years or older with transfusion-dependent α-thalassaemia or β-thalassaemia were randomly allocated (2:1) with a central interactive response technology system, stratified by geographical region and thalassaemia genotype, to receive 100 mg mitapivat or placebo orally twice a day for 48 weeks. The primary endpoint was transfusion reduction response (TRR), defined as a reduction of at least 50% in transfused red blood cell units with a reduction of at least two units in any consecutive 12-week period until week 48 compared with baseline. Efficacy was analysed in the full analysis set, comprising all randomly allocated patients. Type, severity, and relationship of adverse events and serious adverse events were assessed in patients who received at least one dose of study treatment. This study is registered with ClinicalTrials.gov (NCT04770779) and is active but not recruiting. FINDINGS: Between Nov 30, 2021 and May 2, 2023, 305 patients were screened, of whom 258 were randomly allocated (median age 33·5 years [IQR 27·0-44·0]; 136 [53%] female and 122 [47%] male participants). Of 258 patients allocated, 238 (92%) completed the double-blind treatment period. All patients were required to have a safety follow-up approximately 4 weeks after the final dose of study drug, regardless of completion of the double-blind period or continuation into the open-label extension period. In the full analysis set, TRRs occurred in 52 (30%) of 171 patients in the mitapivat group and 11 (13%) of 87 in the placebo group (adjusted difference 18 percentage points [95% CI 8-27]; two-sided p=0·0003). The safety analysis set comprised 172 patients in the mitapivat group (including one patient allocated to the placebo group who received one dose of mitapivat in error) and 85 in the placebo group. Adverse events were reported in 155 (90%) patients treated with mitapivat and 71 (84%) treated with placebo; the most common events with mitapivat were headache, upper respiratory tract infection, initial insomnia, diarrhoea, and fatigue. Serious adverse events were reported in 19 (11%) patients treated with mitapivat and 13 (15%) treated with placebo. Ten (6%) patients who received mitapivat and one (1%) who received placebo discontinued study treatment due to adverse events. No deaths were reported. INTERPRETATION: Mitapivat significantly reduced the transfusion burden and was generally well tolerated, showing a favourable benefit-risk profile. These findings support mitapivat as the first oral disease-modifying therapy for adults with transfusion-dependent α-thalassaemia or β-thalassaemia, providing a new treatment option to reduce transfusion burden in this patient population. FUNDING: Agios Pharmaceuticals, Inc.

Adult↗

PubMed2026

Design, rationale, and baseline patient characteristics for the Sickle Cell Disease and CardiovAscular Risk-Red cell Exchange (SCD-CARRE) trial.

BACKGROUND: Despite wide utilization of automated red blood cell exchange (RBCX) transfusion in adult patients with sickle cell disease (SCD), no consensus or quality efficacy data exist on its use. The Sickle Cell Disease and CardiovAscular Risk- Red cell Exchange (SCD-CARRE) trial tests the hypothesis that an automated chronic RBCX transfusion strategy reduces acute health care encounters and death while improving quality of life and end-organ function (cardiac, pulmonary and renal) in participants with SCD that are at high risk of death. METHODS: Adult patients with SCD with elevated tricuspid regurgitant jet velocity (TRV) and/or chronic kidney disease were considered to be at high risk of death and were randomly assigned to RBCX plus standard of care vs standard of care alone. Participants assigned to RBCX received 12 months of exchange transfusions to maintain target pretransfusion hemoglobin S% < 30%, post-transfusion hemoglobin S% < 20%, and post-transfusion hemoglobin concentration ≥10 g/dL. All study participants were managed according to NHLBI/ASH/ATS Expert Panel guidelines. The primary endpoint was the number of SCD acute health care encounters or death over 13 months. Secondary endpoints included measures of cardiovascular and renal function, exercise capacity, patient reported outcomes (all collected at baseline, and months 4, 8, and 12), and transfusion-related adverse events (collected monthly). RESULTS: Between 2020 and 2025, the SCD-CARRE trial randomized 173 participants at 23 sites across 3 countries. Enrolled participants had mean (SD) age of 45.8 (11.8) years and 54% were female. At baseline, participants had average TRV of 2.8 (0.5) m/s such that 45.9% had a TRV between 2.5 to 2.9 m/sec and 28.1% had a TRV ≥ 3.0 m/sec. The median (Q1, Q3) eGFR in this cohort was 60 (36, 110) mL/min/1.73 m2. The median (Q1, Q3) 6-minute walk test distance was 375 meters (309, 439), the median daily steps were 3,728 (2,187, 5,821), and participants experienced a median (Q1, Q3) of 2 (1, 5) pain episodes in the year prior to randomization. The trial results are pending. CONCLUSIONS: The SCD-CARRE trial successfully enrolled a cohort of n = 173 adults with SCD. This study highlights a rationale to evaluate the effect of automated chronic RBCX transfusion strategy plus standard of care as compared to standard of care alone in SCD patients at high risk of death with a focus on patient centered outcomes, preservation of cardiovascular function, end-organ complications and death. TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT04084080, https://clinicaltrials.gov/study/NCT04084080.

Adult↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Recurrent myocardial infarction identified by centralized troponin review: Insights from the MINT trial.

BACKGROUND: The utility of routine troponin testing to identify recurrent myocardial infarction (MI) after an incident MI is unclear. We assessed the incidence and prognosis of recurrent MIs identified from centralized troponin review in patients from the Myocardial Ischemia and Transfusion (MINT) trial. METHODS: The MINT trial randomized patients with acute MI and anemia to a liberal vs restrictive red blood cell transfusion strategy. Suspected recurrent MIs were identified through both site-report and centralized review of troponin levels collected for 3 days following randomization. Differences in cardiac, noncardiac, and all-cause death at 30 and 180 days were compared across patients with any site-reported MI, only centrally identified MI, and no recurrent MI. RESULTS: Among 3,504 patients, 275 (7.8%) had a recurrent MI within 30 days; 119 (43.3%) by site-report, and 156 (56.7%) by central troponin review only. Rates of cardiac and all-cause death at 30 and 180 days were highest for patients with site-reported MI, intermediate for centrally identified MI, and lowest for no recurrent MI; rates of noncardiac death did not vary. Patients with only centrally identified recurrent MI had an increased risk of cardiac death at 30 days (RR 1.9, 95% CI 1.0-3.4) and 180 days (RR 1.7, 95% CI 1.1-2.7) compared to those without recurrent MI. CONCLUSIONS: In patients with acute MI and anemia, centralized troponin review identified more than half of all recurrent MI events. Patients with centrally identified MI had a higher risk of cardiac death than those with no recurrent MI. TRIAL REGISTRATION: ClinicalTrials.gov NCT02981407 https://clinicaltrials.gov/study/NCT02619136.

Humans↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

Efficacy and Safety of the C3 Inhibitor Pegcetacoplan in Paroxysmal Nocturnal Hemoglobinuria: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the efficacy and safety of the complement C3 inhibitor pegcetacoplan in patients with paroxysmal nocturnal hemoglobinuria (PNH). METHODS: PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies reporting pegcetacoplan use in PNH. Outcomes included transfusion-requirement, hemoglobin normalization, mean hemoglobin levels, lactate dehydrogenase normalization, reticulocyte count normalization, and safety endpoints. Pooled proportions with 95% confidence intervals were calculated using random-effects models, and heterogeneity was assessed using the I2 statistic. RESULTS: Five studies comprising 271 patients were included. Transfusion avoidance was observed in 80.6% of patients, with a pooled transfusion-requirement rate of 19.4%. LDH normalization occurred in 68.5% of patients (I2 = 0%). Hemoglobin normalization was observed in 42.9%, while reticulocyte count normalization reached 66%. Any-grade adverse events occurred in 83.5% of patients, most commonly pyrexia, headache, and dizziness. Serious adverse events occurred in 16.6%, decreasing to 12% after sensitivity analysis. Breakthrough hemolysis was reported in 14.8%, and infections in 17%. CONCLUSION: Pegcetacoplan demonstrates consistent efficacy signals across key hematologic endpoints and an acceptable safety profile, supporting its potential role as an important therapeutic option, particularly in patients with persistent extravascular hemolysis despite C5 inhibition.

Humans↗

PubMed2026

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida↗

PubMed2026

The voice clone intelligibility benefit in noise in middle-aged listeners.

Research with younger adults showed that cloned voices are more intelligible than human voices in noise, with a benefit of 13.4%. This study tested whether this benefit extends to 40 middle-aged listeners (45-65 years), as this population may show emerging difficulties with speech-in-noise. Participants recognised sentences by ten human voices and ten voice clones in four noise levels. Cloned voices were 11.8% more intelligible, with benefits enhanced at the two most severe noise levels (15.9% at -6 dB and 17.5% at -3 dB), suggesting cloned speech enhanced perception in middle-aged listeners, potentially by reducing listening effort and compensating for emerging age-related auditory-cognitive decline.

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

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗

PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗

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↗