± 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
Subscribe

All stories

877 published stories · page 23 of 59

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…

Journal of Radiological Protection · Health

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

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

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
Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment
Evidence-backed gain

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

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

Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation

Background Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and healthcare by enabling analysis of large-scale biomedical data and supporting data-driven decision-making. However, their rapid integration has introduced significant ethical, governance, and implementation challenges that remain insufficiently synthesized within a unified framework. Objective This work aims to synthesize the central ethical challenges and paradoxes associated with AI and ML in pharmace…

Health
Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation

Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development and clinical application of machine learning models based on multimodal big data for precision transfusion management in AML, addressing the persistent limitations of conventional, empirically guided transfusion practices. We systematically reviewed the li…

Health
Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia

Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

BackgroundAccurate triage of trauma patients by Emergency Medical Services (EMS) is essential for optimal outcomes and resource allocation. The 2021 National Field Triage Guidelines (FTG) assist EMS in prehospital triage; however, its collective performance has never been evaluated using a national database. We aimed to evaluate an FTG surrogate and develop a predictive model to identify patients at risk for serious injury.MethodsThe Trauma Quality Improvement Program National Trauma Databank (2017-2020) was que…

Health
Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using…

Health
AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial