Clinical connectomics: Mapping structural brain networks in disease.

The human connectome holds considerable promise for advancing an integrated, systems-level understanding of brain disorders. Critical to this ambition is accurate, reliable, and biologically informati...

Comprehensive RNA-binding protein analyses and deep learning uncover genetic constraints and disease associations in protein-RNA interfaces.

RNA-binding proteins (RBPs) orchestrate post-transcriptional processes, including splicing, cleavage and polyadenylation, and translation. Our updated RBP resource integrates data from 92 additional R...

Integrative genetic and liver transcriptomic analyses identify TRIB1AL as a target for steatotic liver disease.

CONTEXT: Genome-wide association studies (GWAS) have identified dozens of genetic loci linked with metabolic dysfunction-associated steatotic liver disease (MASLD). OBJECTIVE: To identify liver-expres...

Towards autonomous medical artificial intelligence agents.

Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows

Global whole-genome, phylodynamic, and machine-learning analysis of

Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma.

Gliomas are heterogeneous primary central nervous system tumors with diverse molecular subtypes and variable prognosis. A paradigm shift in histopathology is underway as samples are digitized into who...

BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.

Large language models (LLMs) are evolving rapidly and hold great promise for medical applications, yet benchmarking on real-world clinical data such as electronic health records remains limited. Most ...

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer.

Integrating genotype (e.g., transcriptomics), phenotype (e.g., imaging), and tumor microenvironment (e.g., metabolomics) is crucial to elucidating the molecular basis of ovarian cancer. However, there...

Adversarial vulnerability and robustness of deep learning models for panoramic dental X-ray segmentation.

Deep learning-based segmentation has become essential in computer-aided dental diagnosis and treatment planning. However, these models remain highly vulnerable to adversarial perturbations, like small...

Trustworthy and explainable AI for drug discovery.

Extracting and Classifying Drug Discontinuations From Estonian Electronic Health Records: Development and Validation Study.

BACKGROUND: Drug adherence is crucial for chronic disease management, yet treatment discontinuation remains common due to factors such as side effects, inefficacy, or cost. These reasons are often rec...

Making Machine Learning Clinically Useful in Thrombosis and Hemostasis: A Roadmap for Diagnostic Translation.

Artificial intelligence (AI), most often in the form of machine learning (ML), attracts high expectations across medicine and is often discussed as a transformative, rapidly evolving topic. In thrombo...

Leveraging Natural Language Processing and Structured Scoring to Evaluate Operative Note Quality in Surgical Training.

OBJECTIVE: To evaluate resident versus attending operative notes using a two-phase approach combining natural language processing (NLP) diffing and structured assessment for evaluating operative repor...

Machine Learning-Enabled Quantification of Hepatocellular Necrosis in the Liver After Lethal Marburg and Ebola Virus Exposures.

The liver is a key target of pathogenic Marburg and Ebola viruses (MARV and EBOV, respectively), with injury common in severe filovirid disease, yet high-resolution histopathologic quantification is l...

Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia.

BACKGROUND: Due to immunosuppression, mucosal barrier injury, and prolonged neutropenia resulting from both the disease and chemotherapy, along with the frequent use of broad-spectrum antibiotics and ...

Understanding and overcoming innate and acquired MAPK inhibition resistance in anaplastic thyroid cancer.

Anaplastic thyroid cancer (ATC) is highly lethal. Although patients with the BRAF

Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.

BACKGROUND: Myasthenia gravis (MG) is a prototypical antibody-mediated autoimmune disease with variable treatment responses with a need for biomarkers to guide therapeutic decision making. Proteomic p...

Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.

BACKGROUND: Differentiating fungal from chronic sinusitis remains a diagnostic challenge due to overlapping symptoms. OBJECTIVES: This study aims to enhance diagnostic accuracy for fungal sinusitis by...

Artificial exosomes synergistically reshape sepsis immune homeostasis by modulating neutrophil fate and blocking PD-1/PD-L1.

A critical challenge in sepsis treatment lies in its complex immune microenvironment, characterized by concurrent hyperinflammation and immunosuppression. This imbalance is jointly driven by dysregula...

[Empowering narrative medicine with artificial intelligence: a new perspective in pediatric medical education].

This paper systematically explores the integration pathways, challenges, and future directions of artificial intelligence (AI) technology and narrative medicine in pediatric medical education. Due to ...