± 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 34 of 70

Evidence-backed gain

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment

Aim Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor progression, but its role in CRC heterogeneity and the tumor immune microenvironment (TIME) requires systematic investigation. Methods A systematic evaluation of 101 combinations of machine learning and statistical algorithms was conducted within a 10-fold cross-validation framework to develop and validate the optimal model, termed the methionine metabolism-related…

Asia-Pacific Journal of Clinical Oncology · Health

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment
Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging
Evidence-backed gain

Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging

Purpose This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clinical activity score and key patient characteristics in thyroid eye disease. Methods The publicly available TOM500 dataset, consisting of orbital magnetic resonance imaging scans and clinical data from 500 thyroid eye disease patients, was split into training ( n = 360), validation ( n = 100), and test ( n = 40) sets. A ResNet-50 convolutional neural network pretrain…

Health
Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors
Evidence-backed gain

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors

Purpose Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy. Methods A cohort of patients receiving ICI therapy from 2010 to 2023 was identified from the TriNetX database. Cardiac i…

Health
Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis
Evidence-backed gain

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis

Background Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for precise risk stratification. This study aimed to develop a metabolomics-based prediction model to identify high-risk metabolic phenotypes among obese participants and to elucidate the causal metabolic pathways involved. Methods Forty-one-thousand-four-hundred-fifty-nine obese participants were followed for a median of 14.4 years. We integrated multiple machine learning…

Health
Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging
Evidence-backed gain

Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging

Clinical justification remains fundamental to the safe use of imaging involving ionising radiation, requiring a favourable balance between diagnostic benefit and stochastic risk. Concurrently, advances in imaging technology and artificial intelligence have enabled opportunistic identification of additional pathologies beyond the primary indication for imaging. This opinion article discusses how emerging opportunistic osteoporosis detection technologies may eventually transition into clinically justified diagnost…

Health
An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers
Evidence-backed gain

An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers

Background Cervical cancer screening in primary care is hindered by expert pathologist shortages and heavy diagnostic workloads, leading to fatigue-induced misdiagnoses. This study evaluated the diagnostic capacity, subpopulation robustness, and operational efficiency of an interpretable machine learning (ML) tool within a large Chinese healthcare network. Methods A retrospective database of 5,000 women was audited. Archived liquid-based cytology (LBC) digital slides were evaluated via a parallel validation chan…

Health

The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0

The topic’s relevance is related to the need to improve the efficiency of municipal waste management in the context of the development of Industry 4.0, where artificial intelligence (AI) can play a key role in optimizing the processes of sorting, collecting, and recycling waste. The purpose of the study is to study the potential of AI to improve environmental and operational indicators in the field of waste management, as well as to test hypotheses regarding the impact of AI on reducing costs, increasing efficie…

Climate
The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0

Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs

Background Escherichia coli poses a global health threat from increasing β-lactam resistance. This study uses genomic and One-Health data to map resistance patterns and enhance antimicrobial resistance (AMR) prediction and management. Methods This study performed a One-Health whole-genome analysis of 30,554 E. coli isolates from human, animal, and environmental sources, spanning from 2000 to 2025. Publicly available genomic data were retrieved from NCBI, encompassing β-lactam resistance genes, including extended…

Climate
Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs

Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment

Artificial Intelligence (AI) bears potential for improving health care, but this depends on the availability of open-access, realistic, and useful data. To facilitate AI model development in health care we release SynTabFall, a novel synthetic dataset for fall risk assessment. With a total of 745,380 samples and 44 attributes such as demographics, diseases, mobility and cognition related risk factors, this tabular dataset allows for training fall risk prediction models without access to the original patient data…

Science
Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment

Cosmos Health Advances Its Technology-Driven Transformation with an AI-Enabled Subscription Platform Across B2C and B2B Channels

Digital subscription platform brings together the Company’s proprietary consumer health products across consumer (B2C) and corporate (B2B) channelsCustomers can begin through one of three paths: a free nutritionist consultation, an AI-powered assistant, or building their own packageCorporate offering supports employee wellness programs and corporate gifting through multi-recipient management, tiered volume pricing, centralized billing, and dedicated account managementFuture platform enhancements are expected to…

Business
Cosmos Health Advances Its Technology-Driven Transformation with an AI-Enabled Subscription Platform Across B2C and B2B Channels