Real-time aggregation of research papers, AI models, datasets and regulatory updates.
Research Papers
590
From PubMed & arXiv
AI Models
115
Healthcare AI models
Regulatory Updates
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FDA & compliance
Datasets
156
Medical datasets
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RESEARCH HIGHLIGHT
BEGA-UNet: Boundary-Explicit Guided Attention U-Net with Multi-Scale Feature Aggregation for Colonoscopic Polyp Segmentation
Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, yet the generalization of deep learning models under domain shift remains insufficiently explored. We propose Boundary-Explicit Guided Attention U-Net (BEGA-UNet), a boundary-aware segmentation architecture that introduces explicit edge modeling as a structural inductive bias to enhance both segmentation accuracy and cross-domain robustness. The framework integrates three components: an Edge-Guided Module (EGM) with learnable Sobel-initialized operators to capture boundary cues, a Dual-Path Attention (DPA) module that processes channel and spatial attention in parallel, and a Multi-Scale Feature Aggregation (MSFA) module to encode contextual information across multiple receptive fields. Evaluated on the combined Kvasir-SEG and CVC-ClinicDB benchmarks, BEGA-UNet achieves 88.53% Dice and 82.51% IoU, outperforming representative convolutional and transformer-based baselines. More importantly, cross-dataset evaluation demonstrates strong robustness under domain shift, with BEGA-UNet retaining 83.2% of its in-distribution performance--substantially higher than U-Net (64.5%), Attention U-Net (47.5%), and TransUNet (53.1%). In a zero-shot setting on an entirely unseen dataset, the model further maintains 72.6% performance retention. Comprehensive ablation studies indicate that explicit boundary modeling plays a central role in improving generalization, while multi-scale context aggregation further stabilizes performance across domains. Feature distribution analyses support this observation by showing that edge-oriented representations exhibit markedly reduced cross-domain variability compared to appearance-driven features. Overall, BEGA-UNet provides an effective and interpretable solution for robust polyp segmentation, demonstrating that explicit boundary modeling serves as a critical inductive bias for ensuring reliability under clinical domain shifts.
Largest MedGemma model optimized for medical text generation, clinical reasoning, and medical question answering. Trained on medical literature and clinical data.
Existing evidence indicates that children and adolescents experiencing bullying victimization (BV) exhibit mental health deterioration, and such effects can persist into adulthood. As current decision-making tools are scarce, we aim to develop a tool to predict subsequent BV risk among Chinese youth. Data were retrieved from a three-wave prospective study which incorporated into the Mental Health Survey for Children and Adolescents in Yunnan (MHSCAY). Six common machine learning (ML) algorithms were used. We internally validated the models using 500 times bootstrap approach to assess discrimination, calibration, and utility. A total of 5345 participants aged 10–17 years completed the survey. The internal validation showed the logistic regression (LR) model slightly outperformed other ML algorithms and exhibited more evenly distributed individual-level prediction uncertainty. It was therefore selected as the final model, achieving an AUROC of 0.800 (95% CI: 0.785, 0.815), AUPRC of 0.519 (95% CI: 0.483, 0.553), calibration intercept of -0.001 (95% CI: -0.076, 0.069), calibration slope of 0.990 (95% CI: 0.930, 1.059), and Brier score of 0.122 (95% CI: 0.117, 0.128). Furthermore, the calibration plot indicated excellent precision, and positive net benefits were observed across broad threshold ranges. Fairness analysis revealed no predictive bias in key subpopulations. This novel predictive tool utilizes seven baseline predictors that are readily accessible to generate accurate, individualized predictions of subsequent BV risk in children and adolescents. Upon further validation, the model may facilitate risk stratification, thereby guiding resource allocation and informing targeted interventions for potential BV crises among Chinese children and adolescents.
Spinal cord injury (SCI) is a highly disabling central nervous system disease with complex pathology, and targeted neuroprotective drugs remain clinically lacking. However, traditional molecular target screening and drug prediction methods are inefficient, costly, and poorly targeted, failing to meet clinical precision treatment needs. To address this, we introduced machine learning to construct a multi-dimensional data integration framework. First, we established normal, acute- and subacute-phase SCI mouse complete transection models, and RNA-seq combined with single-cell sequencing revealed acute-phase may occur extensive neuronal PANoptosis. Using WGCNA and MCC algorithms, 25 candidate genes for extensive neuronal PANoptosis in the acute phase were screened out. Then, we comprehensively applied machine learning algorithms including Elastic Net-GLM, Random Forest, Support Vector Machine, and LASSO to predict and prioritize potential molecular targets, identifying 13 possible core genes for extensive neuronal PANoptosis, including Tacc3, Aurka, Mcm6, Mcm5, Ripk1, etc. With the help of the Connectivity Map, drug prediction was performed on these 13 genes, and the 8 candidate drugs with neuroprotective effects were screened out. Through protein domain screening, it was verified via proof-by-contradiction assays that the drug Xaliproden can establish robust interactions with the 7XMK, 7FCZ and 7FD0 domains of Ripk1, a core molecule of the PANoptosome, via a network of multiple hydrogen bonds. This finding provides a novel screening strategy for neuroprotective drugs for spinal cord injury and is of great significance for promoting the establishment of a precision treatment system for the acute phase of injury.
Non-pedunculated colonic neoplasia (NPCN) is increasingly encountered due to expanded bowel cancer screening and improvements in high-definition endoscopy. Flat and sessile lesions carry higher risks of incomplete resection, recurrence and submucosal invasion than pedunculated polyps, making accurate optical diagnosis and appropriate technique selection essential for high-quality care. This review synthesises evidence from 2016 to 2026 providing a contemporary practice-focused update for clinicians delivering endoscopic resection services.
Advances in optical characterisation, including Narrow-band imaging International Colorectal Endoscopic classification, Japan NBI Expert Team classification and Kudo pit pattern classifications, have improved real-time prediction of histology and invasion depth, supporting decision-making between cold resection, endomucosal resection (EMR), endoscopic submucosal dissection (ESD) and surgical referral. Cold snare polypectomy and cold EMR have become preferred techniques for small and intermediate lesions due to excellent safety profiles and high complete resection rates. For larger lesions (≥20 mm), piecemeal EMR with systematic margin ablation using snare-tip soft coagulation now represents the standard of care, reducing recurrence to below 10%.
Emerging techniques such as underwater EMR, cap-assisted EMR and endoscopic full-thickness resection expand therapeutic options for fibrotic or non-lifting lesions. ESD remains crucial for en bloc resection when superficial submucosal invasion is suspected, though its use varies across the UK and international practice due to differences in training pathways and service configuration.
THE International College of Surgeons, Nigeria Section (ICS-NG), has urged its members and other healthcare professionals to embrace Artificial Intelligence (AI) to enhance surgical training and healthcare service delivery.
Speaking at the opening of the college’s 59th Annual General Meeting and Scientific Conference in Abuja, the President of ICS-NG, Prof. Bernard Jiburum, said AI had become an integral part of modern surgical practice and medical education globally.
Jiburum said the technology was especially important for Nigeria, which continues to grapple with the migration of skilled health professionals.
According to him, AI offers opportunities for continuous learning, improved access to training and better healthcare outcomes.
The conference is themed: “Robotic Surgery: Role of Innovations and Technology in Global Surgery Trends and AI in Global Surgery Evolution”.
A sub-theme focuses on “Emotional Intelligence and Psychosocial Aspects of Surgical Practice in a Global Community”.
Jiburum described the themes as timely and relevant, given Nigeria’s healthcare challenges and the growing role of technology in addressing them.
He noted that AI had applications across diagnosis, surgical procedures, training and post-operative care, adding that its relevance had become more evident in the face of disease outbreaks such as COVID-19, Lassa fever and Ebola.
“In all aspects of surgery, AI is improving training, access to training and evaluation. We are using it to train better and to improve healthcare delivery,” he said.
The ICS president also expressed concern over stress and burnout among surgeons and trainees, attributing the situation to manpower shortages, inadequate facilities and poor remuneration.
He urged government to address the impact of economic reforms on the welfare of healthcare workers who remained in the country to provide services and train younger professionals.
Also speaking, Dr Ityo-Aker Kenneth, a consultant orthopaedic surgeon and medical director, called for wider adoption of AI in the medical profession, stressing the need for ethical considerations and local adaptation.
According to him, most AI systems currently used in Nigeria are developed abroad and may not adequately reflect local realities.
“We need to develop our own algorithms and feed local data into AI systems so they can provide solutions that address our peculiar needs,” he said.
Kenneth added that AI could help healthcare professionals extend quality services to underserved and remote communities.
A former President of ICS-NG, Prof. Akanimo Essiet, called on government to provide greater support for healthcare institutions and professional bodies.
Essiet, who is also a member of the World Executive Committee of ICS Global and Secretary of the African Federation of ICS Global, said improved healthcare funding would strengthen service delivery and professional development.
Similarly, the President-Elect of ICS-NG, Dr Grace Nwana, urged government to provide psychosocial and institutional support for healthcare workers.
She said healthcare professionals required adequate remuneration, modern equipment, research opportunities and a conducive working environment to perform optimally.
“When healthcare workers have the necessary infrastructure, equipment, educational opportunities and support systems, they are better positioned to deliver quality services,” Nwana said. (NAN)
A.I
Midjourney announced Midjourney Medical, a new division focused on full-body imaging.
The company said its proposed "Ultrasonic CT" system could ultimately generate MRI-like scans in about 60 seconds using soundwaves, water, and AI.
Midjourney plans to open its first imaging-equipped spa in San Francisco in 2027 and eventually deploy tens of thousands of scanners worldwide.
Immune checkpoint blockade therapy has revolutionized cancer treatment and demonstrated significant clinical efficacy. However, conventional monoclonal antibody therapeutics still face numerous limitations. Peptide inhibitors, with their low molecular weight, ease of synthesis, cost-effectiveness, and minimal immunogenicity, offer a promising alternative by combining the high specificity of antibodies with the favorable tissue penetration of small molecules. As such, they represent a key direction for overcoming existing therapeutic bottlenecks and developing next-generation immunotherapies. Despite facing key challenges in clinical translation, particularly regarding metabolic stability and oral bioavailability, peptide-based inhibitors hold considerable potential to bridge the gap between antibodies and small-molecule drugs, positioning them as an important component of next-generation cancer immunotherapy. Currently, research in this field is increasingly shifting from traditional empirical screening to intelligent precision design, employing strategies such as rational design based on hotspot amino acids, AI-assisted drug discovery, and advanced delivery systems to optimize the activity, stability, and targeting properties of peptides. This review systematically outlines recent advances in immune checkpoint peptide-based inhibitors, aiming to provide a theoretical foundation for the rational design and clinical translation of this emerging class of therapeutics.
Aging populations face growing multimorbidity, while episodic clinical assessments fail to capture gradual physiological changes unfolding during daily life. Although wearable technologies enable continuous monitoring, single-modality systems provide incomplete and context-limited insight. This Perspective focuses on hybrid wearable sensors that integrate physical and chemical sensing for geriatric healthcare. Hybrid wearable sensing provides a pathway toward continuous, predictive, and personalized geriatric health management. By monitoring continuously multiple health parameters, such multimodal systems have distinct advantages for real-time monitoring, including early risk detection and more personalized health assessment through the integration of complementary physical and biochemical signals. We discuss recent advances in wearable physical sensors, alongside with emerging wearable chemical sensors, then argue that chem-phys hybrid integration enables more interpretable and clinically actionable assessment of aging trajectories than single-modality wearable systems. Finally, we discuss translational requirements and future prospects, including robust real-world operation, AI-driven inference, and integration with telemedicine and home-based care.
Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability of machine learning performance and the recurrence of biological signals across datasets require cautious interpretation. We developed an explainable artificial intelligence framework spanning two fluids and four ADNI proteomic datasets, covering 2082 modality specific samples, all analysed internally within ADNI. Phase 1 analysed plasma using a 119 analyte NULISA and targeted UPENN panel (n = 727; 216 CE, 511 controls). Phase 2 extended the analysis to CSF using SOMAscan7k, TMT-MS and targeted SET2, with Elecsys Aβ42, Aβ40, total tau and p-tau181 as anchor biomarkers. Only SOMAscan was subject-independent relative to Phase 1 plasma; TMT-MS and SET2 overlapped with Phase 1 for 96.0% and 97.7% of subjects and therefore are not independent replication cohorts. Under subject-level splits with fold internal preprocessing, we compared Elastic Net, Explainable Boosting Machines and gradient boosted trees with SHAP-based explanations.
Among the candidate pipelines, we selected the pipeline with the highest held-out test ROC AUC for each platform; the selected values were 0.927 in plasma and 0.954–0.973 across the three CSF datasets. Because the same held out test performance was used for pipeline selection and headline reporting, these are optimistically selected single-holdout estimates, not unbiased estimates of generalizable or clinical performance. Explanations identified five recurring biological axes within ADNI: cholinergic (ACHE), tau/14–3-3 (YWHAG, YWHAZ, YWHAB, YWHAE), neuro-axonal (NEFL, NEFH), microglial/complement (CHIT1, SMOC1, CHI3L1, C7, CFH) and synaptic (NPTXR, NPTX2, DLG4, SYT5, VSNL1, ELAVL2). CSF analyses showed synaptic vesicle-cycle enrichment (q = 2 × 10−6), and CSF YWHAG correlated strongly with total tau (ρ = 0.87). Cross-fluid directional concordance was modest overall (54–57%) but increased to 73–80% among mapped analyte/protein rows reaching q < 0.05 in CSF. These findings provide hypothesis-generating, internally supported evidence within ADNI. Independent external cohorts with locked pipelines are required to evaluate generalizable performance and biological reproducibility; the overlapping TMT-MS and SET2 analyses should not be interpreted as independent replication.
Endometrial cancer is the most common gynecologic cancer, with more than 69,000 cases diagnosed in the U.S. in 2025 and increasing up to 3% annually. Diagnosis requires an often painful and invasive biopsy that carries a risk of false negatives. A multidisciplinary research team at Washington University in St. Louis and Siteman Cancer Center, based at Barnes-Jewish Hospital and WashU Medicine, is looking to a fast, safe and noninvasive imaging method combined with machine learning for an accurate detection and diagnosis of precancerous lesions and early cancers.
The team, led by Quing Zhu, the Edwin H. Murty Professor of Engineering in the McKelvey School of Engineering at Washington University in St. Louis, conducted an initial investigation using optical coherence tomography (OCT), which detects differences in how tissue reflects light and acquires high-resolution 3D images with a depth of up to 1 to 2 millimeters. With a custom catheter probe developed in Zhu's lab, the team took images of the entire endometrial cavity in less than 3 seconds, creating an optical biopsy. It is the first catheter-based, 3D OCT imaging study that integrated optical functional, structural and radiomic features for endometrial assessment.
Success with nursing AI will depend on designing tools around the realities of bedside care, governance and operational outcomes – rather than adapting physician documentation technologies.
