All stories

1045 published stories · page 11 of 70

Both readings

The Role of Artificial Intelligence Models in Predicting Post-Prosthetic Facial Esthetics in Edentulous Patients: Clinical and Anthropometric Comparative Study

Objectives Artificial intelligence (AI) is increasingly used in esthetic dentistry; however, its accuracy in predicting post-prosthetic facial outcomes in edentulous patients remains unclear. This study aimed to evaluate the ability of AI models to simulate post-denture facial esthetics compared with actual clinical outcomes. Materials and methods A prospective within-subject observational study was conducted on 14 completely edentulous patients receiving new complete dentures. Standardized facial photographs we…

European Journal of Dentistry · Health

Evidence-backed gain

Ai-based multimodal analysis of ECG and clinical data for evaluation for competitive sports participation: The VALETUDO trial

Aims Pre-participation cardiovascular screening (PPS) is essential for preventing SCD in athletes, yet ECG interpretation requires expertise and remains resource-intensive. We aimed to evaluate the feasibility and diagnostic performance of a deep learning (DL) model for analysis of clinical data and resting 12‑lead ECG obtained during routine PPS in competitive athletes. Methods In this prospective single center observational study, competitive athletes aged 18 to 60 years and undergoing routine PPS were enrolle…

Health

Evidence-backed gain

The impact of frailty and its changes on the development of motoric cognitive risk syndrome in middle-aged and older adults

Background Previous research links frailty to cognitive decline, but the relationship between frailty and motoric cognitive risk syndrome (MCR), a dementia precursor, is underexplored. Methods This study used CHARLS data with 3388 participants aged ≥45. Data from 2011 to 2012, 2013, and 2015 were analyzed to examine the relationship between frailty index (FI), total FI, and changes in FI (△FI) with MCR risk. Nine machine learning models were built using baseline FI to predict MCR risk, and changes in frailty sta…

Lifestyle

Evidence-backed gain

Anticipating health and care trajectories from routinely collected social care records

Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, remain underexplored in this domain. In this study, we apply machine learning to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire, encompassing around 90% of individuals receiving care in the region. We developed models to predict three outcomes of interest: future care plan needs, hospital admissions, and all-cause mortality, ev…

Health

Evidence-backed gain

Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis

Background Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify children requiring early hemodynamic escalation after fluid resuscitation. Methods We performed a retrospective multicenter study using electronic health record data from five pediatric emergency departments (March 2022-February 2025). Children aged 3 months-17 years screened for sepsis who received ≥2 fluid boluses and were vasopressor-naïve at the second bolus were in…

Health

Both readings

Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening

Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an artificial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSO…

Health

Both readings

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty

Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial assessment tools. Machine learning (ML) is increasingly used to map PTE concentrations from environmental covariates, but many studies still rely on spatially naive validation, limited interpretation, and incomplete uncertainty reporting. This review synthesizes recent advances (2020-2025) in ML-based PTE mapping with emphasis on four requirements for monitoring-grad…

Climate

Deepfake Jimmy Kimmels and Jon Stewarts are everywhere : NPR

While social media is rife with AI celebrity deepfakes, videos featuring late night TV hosts like Jimmy Kimmel and Jon Stewart are are easier to create than other kinds of deepfakes and potentially more disruptive.

Media & Arts

Deepfake Jimmy Kimmels and Jon Stewarts are everywhere : NPR

AI isn't making art. It’s selling us fake intimacy

AI art is an empty simulation detached from reality. True art demands human consciousness, lived struggle, and mutual recognition.

Media & Arts

AI isn't making art. It’s selling us fake intimacy

Advanced AI threatens global financial stability, says Bank of England boss

The Bank of England’s governor, Andrew Bailey, has joined the throng of figures warning about the global risks posed by the most advanced artificial intelligence technology. In a two-page letter sent to international finance ministers and central bank governors as part of his role as chair of the international Financial Stability Board (FSB), Bailey said “frontier” AI models were “showing increasingly sophisticated autonomy and problem-solving abilities, as well as threat capabilities”. He said the models risked…

Policy

Advanced AI threatens global financial stability, says Bank of England boss

ARTIFICIAL INTELLIGENCE AND THE FUTURES TURN: an anticipatory infrastructure for qualitative methods

In this article, I focus on artificial intelligence (AI) in a social science futures research agenda. This agenda is proposed in response to a contemporary context where global future uncertainties are generating a futures knowledge market increasingly populated by the promise of faster and scaled-up AI foresight. Acknowledging the possibilities offered by technological and interactional focuses in developing AI methods, I turn to reflexively discuss the “side effects” of using AI methods in qualitative futures-…

Science

Clinical phenotyping of bloodstream infections: a review of current evidence

Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effective patient stratification and treatment optimisation. In other heterogeneous conditions such as sepsis, data-driven clinical subphenotyping has identified reproducible subgroups with distinct outcomes and treatment responses. Whether similar approaches can be applied to BSIs to improve clinical management and trial design is an area of growing interest. We aimed to review t…

Health

Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning

Most existing concentration-based risk assessments of potentially toxic elements (PTEs) in farmland soils tend to overlook source-specific transport pathways and receptor-related risks. In this study, an integrated risk index-machine learning framework based on the source-pathway-receptor concept is developed to comprehensively evaluate PTE risks in a typical mining city. The framework integrates the improved Nemerow index (INI), potential ecological risk index, Monte Carlo simulation-based health risk assessmen…

Climate

Musicians-turned-detectives are hunting for AI grifters

The spread of AI-generated EDM music is creating a culture of callouts and mistrust among artists.

Media & Arts

Musicians-turned-detectives are hunting for AI grifters

Development and validation of a generalizable M-protein screening model using routine laboratory indicators: a multicenter retrospective study

Early detection of plasma cell disorders (PCDs) remains challenging due to limited accessibility of gold standard diagnostic methods. This study aimed to develop a simple M-protein screening model using routine laboratory indicators for clinical laboratories. A total of 5217 participants from three Chinese hospitals were enrolled. The derivation cohort (n = 3019) was randomly divided into training and internal validation cohorts. Two external validation cohorts (n = 1747 and n = 451) were included. M-protein pos…

Science

Development and validation of a generalizable M-protein screening model using routine laboratory indicators: a multicenter retrospective study