+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
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454 stories · page 11 of 31

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Multi-omics strategies for biomarker discovery and application in personalized oncology

Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology. Despite rapid technological developments, a comprehensive synthesis addressing integration strategies, analytical workflows, and translational applications has been lacking. This review presents a comprehensive framework of multi-omics integration, encompassing workflows, analytical techniques, and computational tools for bot…

Molecular Biomedicine · Health

Multi-omics strategies for biomarker discovery and application in personalized oncology
Beyond Visual Scoring: Computational CT-analysis for HRCT based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease
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Beyond Visual Scoring: Computational CT-analysis for HRCT based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Interstitial lung disease (IRD-ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRD). High-resolution computed tomography (HRCT) is widely considered the gold standard for the non-invasive assessment of ILD; however, its interpretation is constrained by substantial inter-observer variability and the need for time-consuming expert evaluation. Computer-based image analysis including artificial intelligence (AI) has emerged as a promising approach for the aut…

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Challenges in Medical Algorithmic Fairness
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Challenges in Medical Algorithmic Fairness

Artificial intelligence (AI) is increasingly being integrated into oncology for applications including cancer detection, risk stratification, treatment planning, and clinical documentation. Concerningly, growing evidence demonstrates that AI systems can reproduce or amplify existing disparities across patient populations. Although considerable effort has focused on developing computational methods to reduce algorithmic bias, many challenges surrounding fairness extend beyond technical implementation. In this com…

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Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation
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Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation

Prostate magnetic resonance imaging (MRI) reporting is a high-impact communication task because small differences in lesion laterality, sector localization, lesion size, Prostate Imaging-Reporting and Data System (PI-RADS) categorization, or staging language can change biopsy targeting, surveillance, counseling, and treatment planning. At the same time, widespread patient-portal access means that many patients encounter radiology reports before clinical discussion and may seek explanations from public large lang…

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Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy
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Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy

To investigate the clinical performance of a novel deep-learning based image reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR), for assessing gastric cancer (GC) on CT. We prospectively enrolled 132 GC patients without history of treatment, all of whom subsequently underwent surgical resection or staging laparoscopy. All patients underwent preoperative abdominal contrast-enhanced CT examinations, and images were reconstructed with both hybrid iterative reconstruction (HIR)…

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IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images
Evidence-backed gain

IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images

Spinal cord injury (SCI) is a serious medical condition. Spinal Cord Injury limits the movement of the body, blocks the nervous system and affects the quality of life of an injured patient. Accurate detection and classification of these fractures are essential for timely diagnosis and treatment planning; however, conventional assessment methods often struggle with noise, variability, and subtle injury patterns in CT imaging.This study aimed to develop an integrated deep learning framework for accurate and robust…

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A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma
Evidence-backed gain

A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma

PURPOSE: To develop a deep learning model for the preoperative, non-invasive identification of germinomas in the pineal region. This retrospective study included 114 patients with pathologically confirmed pineal region tumors. The cohort was randomly divided into a training set (n = 91) and a test set (n = 23). The training set was further partitioned into three folds for cross-validation. A convolutional neural network (CNN) enhanced with contrastive learning was used to extract discriminative features from ind…

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A comprehensive review on automated diabetic retinopathy detection and classification using fundus image

Diabetic Retinopathy (DR) is a vision-threatening complication in diabetic patients. It harms retinal vessels and may lead to blindness. Detection at an early stage and its classification can prevent the risk of vision loss. However, fundus image-based manual screening of DR is a time-consuming and complex process. In recent years, many automated techniques for DR detection have been developed to screen and diagnose the disease condition at an early stage. These techniques are explored using the keywords diabeti…

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A comprehensive review on automated diabetic retinopathy detection and classification using fundus image

Clustering of rheumatoid arthritis patients: an unsupervised machine learning approach for characterizing the TNFi response

This study aimed to identifydistinct profiles of response to Tumor Necrosis Factor inhibitors (TNFi) in rheumatoid arthritis (RA) patients using unsupervised machine learning to integrate clinical, demographic, and genetic data. A cohort of 294 RA patients was analyzed using hierarchical clustering techniques. Data collected included body mass index (BMI), adherence to physical activity, prevalence of comorbidities, seropositivity, Health Assessment Questionnaire (HAQ) scores, and genetic polymorphisms in TNF pa…

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Clustering of rheumatoid arthritis patients: an unsupervised machine learning approach for characterizing the TNFi response

A framework for evidence-based psychotherapy with AI (EBP-AI)

Artificial intelligence (AI) systems and large language models offer substantial potential to augment or even fundamentally change elements of psychological assessment and treatment. However, current AI technologies have yet to demonstrate the capacity to effect meaningful and sustained clinical change. This gap reflects both the limited integration of clinical science knowledge into language models and applications built using them, as well as the mismatch between the brief, minutes-long nature of most AI inter…

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A framework for evidence-based psychotherapy with AI (EBP-AI)

An AI-Supported Virtual Patient Application Using a Structured Prompting Framework to Develop Mental Status Examination Skills in Psychiatric Nursing Internship Students: An Interventional Mixed-Methods Study

The aim of this study is to evaluate the effect of an artificial intelligence (AI)-powered virtual patient application (ChatGPT) on the development of Mental Status Examination (MSE) skills in psychiatric nursing internship students and to explore how this process is reflected in their experiences. This study employed a sequential explanatory mixed-method design, consisting of a quantitative pre/post-test phase followed by a qualitative phase. In the quantitative phase, the MSE Competency Assessment scores of 27…

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An AI-Supported Virtual Patient Application Using a Structured Prompting Framework to Develop Mental Status Examination Skills in Psychiatric Nursing Internship Students: An Interventional Mixed-Methods Study

Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis

Tuberculosis (TB) remains a leading cause of infectious disease mortality worldwide, and treatment failure contributes to ongoing transmission, drug resistance, and poor clinical outcomes. Artificial intelligence (AI) and machine learning (ML) approaches have attracted growing interest in predicting TB treatment outcomes, but the literature is heterogeneous and lacks a comprehensive synthesis. We systematically searched PubMed/MEDLINE and Embase (January 2000-October 2025) for studies developing or validating AI…

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Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis

Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect…

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Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives

Spontaneous intracerebral hemorrhage (ICH) is a highly lethal and disabling form of stroke, in which hematoma expansion (HE) is a major and potentially modifiable determinant of early neurological deterioration and poor functional outcome. Computed tomography (CT) remains the first-line imaging modality for acute ICH and provides essential information for early HE risk stratification. However, current evidence is dispersed across conventional CT signs, composite scores, radiomics, machine learning, and deep lear…

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CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives

Artificial Intelligence and Nursing: Freedom From the Mundane or Subservience to a New Nobility

Aim To explore the notion of post-humanism and the impact of artificial intelligence (AI) on society, nursing and healthcare. Design Discursive paper. Methods Critical reflection on concepts relating to post-humanism and the impact of AI, sourced from contemporary and established literature. Data sources Information was drawn from a wide range of empirical and theoretical resources, including health services research through to sociology and philosophy, including newspapers, popular science as well as peer revie…

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Artificial Intelligence and Nursing: Freedom From the Mundane or Subservience to a New Nobility