± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
TruaceTracing the truth around AIFriday, August 28, 2026
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877 published stories · page 7 of 59

Evidence-backed gain

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…

Arthritis Care & Research · Health

Beyond Visual Scoring: Computational CT-analysis for HRCT based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease
Challenges in Medical Algorithmic Fairness
Both readings

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…

Health
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…

Health
Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy
Evidence-backed gain

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)…

Health
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…

Health
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…

Health
A comprehensive review on automated diabetic retinopathy detection and classification using fundus image
Evidence-backed gain

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…

Health

Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

This study evaluates an innovative field-scale targeted sampling strategy within a regional hybrid spatial prediction model that combines machine learning and geostatistics. The framework is designed so that newly collected field observations are incorporated only through the local residual kriging step, while the regional Random Forest trend model remains unchanged, allowing field-scale predictions to be refined without full model refitting. The proposed sampling approach integrates a Normalized Difference Vege…

Climate
Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

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…

Health
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…

Health
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…

Health
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

Quantum-Enhanced Transfer Learning for IDR Binding Partner Prediction

Intrinsically Disordered Regions (IDRs) play essential roles in cellular processes through interactions with proteins, nucleic acids, lipids, and metal ions, yet predicting their binding partners remains challenging for understanding protein function and drug discovery. However, current computational methods including protein language models face performance plateaus where traditional approaches to improve accuracy have become ineffective. Here, we present a hybrid quantum-classical machine learning approach tha…

Science
Quantum-Enhanced Transfer Learning for IDR Binding Partner Prediction

Exploring fashion designers' acceptance of AIGC: A dual-pathway analysis from the stimulus-organism-response perspective

Artificial Intelligence Generated Content (AIGC) is increasingly used in creative design. Understanding fashion designers' willingness to adopt these tools has therefore become important for both research and practice. Drawing on the Stimulus-Organism-Response (SOR) model, this study integrates Self-Determination Theory (SDT) with perceived risk, social influence, and facilitating conditions. It examines how these contextual stimuli shape designers' basic psychological need satisfaction and behavioral intention.…

Lifestyle
Exploring fashion designers' acceptance of AIGC: A dual-pathway analysis from the stimulus-organism-response perspective

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…

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

Marine agency AI art contest sparks backlash

Strong criticisms were lobbed at the Department of Marine and Coastal Resources after it announced the results of an AI art contest held to mark National Dugong Conservation Day on Monday, with the winning image showing a dugong with what appeared to be four legs.

Media & Arts
Marine agency AI art contest sparks backlash