± HEALTH Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and gener…+ 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…
TruaceTracing the truth around AIMonday, September 14, 2026
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1046 published stories · page 23 of 70

Evidence-backed gain

CURL-AID: Automated Echocardiographic Motion Analysis for Quantitative Assessment of Posterior Systolic Curling

Objective Posterior systolic curling (PSC) is a morphofunctional abnormality of the posterior mitral annulus associated with malignant ventricular arrhythmias and sudden cardiac death. Current diagnosis is qualitative and operator-dependent, limiting reproducibility, objectivity and standardization. This study introduces CURL-AID (Curling Ultrasound-based Recognition and Labeling-Automated Intelligence-driven Diagnosis), a fully automated echocardiographic framework for PSC detection through quantitative analysi…

Ultrasound in Medicine & Biology · Health

CURL-AID: Automated Echocardiographic Motion Analysis for Quantitative Assessment of Posterior Systolic Curling
Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro
Evidence-backed problem

Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro

Purpose Lumbar disc herniation is associated with substantial morbidity, including low back pain, radicular leg pain (sciatica), sensory disturbance, and motor deficit. Magnetic resonance imaging (MRI) is central to confirming the diagnosis in symptomatic patients and to planning surgical or interventional management. Recent advances in artificial intelligence (AI) raise the possibility of automating aspects of image interpretation to improve consistency and reduce radiologist workload. This study evaluates a ge…

Health
Advances in AI's Future in Toxicology: Integrating Computational Prediction and Clinical Translation Through Explainable Artificial Intelligence
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Advances in AI's Future in Toxicology: Integrating Computational Prediction and Clinical Translation Through Explainable Artificial Intelligence

Artificial intelligence (AI) and machine learning are increasingly used in toxicological risk assessment to predict chemical toxicity, identify hazardous compounds, and support regulatory decision-making. However, the widespread adoption of these models is limited by their "black-box" nature, which reduces interpretability, transparency, and regulatory confidence. Explainable artificial intelligence (XAI) has emerged as a promising approach to address these challenges by revealing how input features, including c…

Health
Machine learning force field development and basic physical property studies for molten salt reactor fuel salt LiF-BeF<sub>2</sub>-UF<sub>4</sub>
Evidence-backed gain

Machine learning force field development and basic physical property studies for molten salt reactor fuel salt LiF-BeF<sub>2</sub>-UF<sub>4</sub>

As one of the most promising technological pathways for Generation IV advanced reactors, molten salt reactors (MSRs) rely on the fuel salt LiF-BeF 2 -UF 4 (FLiBeU), whose microstructural characteristics and fundamental physical properties determine the reactor's thermal-hydraulic behavior and safe operating limits. In response to the experimental challenges posed by the high temperature and high radioactivity of this molten salt system, this study adopts the deep potential molecular dynamics (DPMD) method combin…

Science
Additional sectioning and AI-assisted diagnosis reveal underdiagnosis of serous (pre)malignancies in fallopian tube specimen from BRCA1/2 carriers
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Additional sectioning and AI-assisted diagnosis reveal underdiagnosis of serous (pre)malignancies in fallopian tube specimen from BRCA1/2 carriers

Aim Germline BRCA1/2 pathogenic variant carriers are at increased risk for high-grade serous carcinoma (HGSC) and are therefore advised to have a risk-reducing salpingo-oophorectomy (RRSO) around the age of 40. A risk of 0.9% to develop peritoneal HGSC (pHGSC) remains, which increases up to 27.5% when serous tubal intraepithelial carcinoma (STIC) is detected at RRSO. The relationship between the detection of STIC and the occurrence of pHGSC is still poorly understood. Here, we investigated the role of tissue sam…

Health
Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns
Evidence-backed gain

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns

Background Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. Objective To develop a novel oxidative stress index score integrating γ-glutamyl transferase and total bilirubin, evaluate its prognostic value in locally advanced rectal cancer patients receiving neoadjuvant therapy, and construct and validate machine learning-based survival prediction models incorporating oxidative stress index score. Design A novel liver enzyme indicator - oxidative stress index score…

Health
Effect of Deep Learning Training Policy on Greenhouse Gas Emissions and Carbon Efficiency for Chest Radiograph Classification
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Effect of Deep Learning Training Policy on Greenhouse Gas Emissions and Carbon Efficiency for Chest Radiograph Classification

Purpose Environmental sustainability is an emerging priority in radiology, yet the impact of deep learning training policies on greenhouse gas emissions remains poorly characterized. This study quantified the effect of training policy on carbon dioxide equivalent (CO 2 eq) emissions and model performance for chest radiograph classification. Methods Anteroposterior chest radiographs (128 907 training, 24 570 validation, 8282 test) were used to train 3 ImageNet-pretrained convolutional neural networks (ResNet-50,…

Climate

Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider

Introduction Patients with musculoskeletal complaints often search online to identify an appropriate healthcare provider. With the increasing availability of large language models (LLMs), these artificial intelligence (AI) tools can direct patients to providers. This study evaluated the ability of LLMs to recommend appropriate providers based on representative patient musculoskeletal queries. Methods Three LLMs (ChatGPT, DeepSeek, and Gemini) were prompted with standardized musculoskeletal queries for two US cit…

Health
Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider

The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Diagnostic errors are a substantial source of patient harm. As artificial intelligence (AI) integrates into clinical workflows, opportunities are emerging to assess their impacts on diagnostic excellence (DxEx). The Coordinating Center for Diagnostic Excellence (CODEX) at the University of California San Francisco established the Action Incubator to translate research advances in DxEx into tangible strategies for improving diagnosis. The September 2025 in-person inaugural Action Incubator convened 30 multidiscip…

Health
The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda

Introduction Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance diagnostic efficacy, efficiency, and patient and provider safety. Methods We convened a two-day workshop with 18 interdisciplinary experts from three countries. Experts represented quality and patient safety, human factors and systems engineering, radiology and other medical specialties, nursing, medical informatics, cognitive and perceptual psychology, psychometri…

Health
Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda

Twitch sparks gamers' wrath with Amazon AI sharing deal

PARIS (FRANCE) - Livestream gaming giant Twitch has raised hackles among its millions of users after declaring it will share their data with its parent company Amazon, to better train the online retailer's AI models.

Media & Arts
Twitch sparks gamers' wrath with Amazon AI sharing deal