+ 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…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
TruaceTracing the truth around AIMonday, September 14, 2026
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1045 published stories · page 20 of 70

Evidence-backed gain

Beyond concentration-based analysis: explainable AI identifies environmental settings influencing urban PM 10 variability

This study applies an explainable artificial intelligence framework to investigate PM10 variability using routine regulatory air-quality data from a single monitoring station, targeting data-limited conditions. A four-year dataset (2020-2023) of PM10, PM2.5, NO2, SO2, O3, and meteorological predictors was analyzed using ensemble machine-learning models with metaheuristic hyperparameter optimization. The best-performing model achieved high predictive performance (R2 = 0.913), supporting model-based interpretation…

Science of The Total Environment · Climate

Beyond concentration-based analysis: explainable AI identifies environmental settings influencing urban PM 10 variability
Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning
Evidence-backed gain

Identifying causal pathways and risk-decision rules for nitrous oxide emission hot moments in wastewater treatment plants using probabilistic causal machine learning

Nitrous oxide (N_2O) emissions from biological wastewater treatment represent a significant challenge for climate-responsible operation due to their intermittency and occurrence as short-lived emission hot moments. Effective mitigation therefore requires accurate prediction and systematic identification of causal pathways and operational risk conditions. This study develops a probabilistic causal machine learning framework based on long-term online monitoring data from a full-scale wastewater treatment plant for…

Climate
Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs
Evidence-backed gain

Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs

Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural assessment and clinical observation. To address this limitation, this study proposes a dual-domain computational framework for automated EEG-based ASD classification by integrating complementary time-frequency analysis with Horizontal Visibility Graph (HVG)-based network modelling. Four time-frequency decomposition techniques, namely the Short-Time Fourier Transform (…

Health
An Introduction to the Machine Learning Lifecycle for Clinical Microbiology
Both readings

An Introduction to the Machine Learning Lifecycle for Clinical Microbiology

Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial intelligence (AI) provides powerful computational methods to gain diagnostic, biological, and epidemiological insights from these complex data. This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and machine learning, inclu…

Health
A Combined Deep Learning Approach to Screen Patients for Neuromuscular Pathology
Evidence-backed gain

A Combined Deep Learning Approach to Screen Patients for Neuromuscular Pathology

Neuromuscular diseases (NMD), comprising over 600 different conditions, severely impact nerve and/or muscle function and lead to significant morbidity. Ultrasound is a non-invasive tool that is gaining acceptance for diagnosing NMD. In clinical practice, muscle ultrasound can be evaluated quantitatively or visually using an ordinal four-point grading score (Heckmatt score). Its current application is limited by time investment in manual analysis and lack of result transferability to other centers. Here, we prese…

Health
Validation of artificial intelligence-assisted CBCT analysis for predicting inferior alveolar nerve proximity to impacted mandibular third molars: a diagnostic accuracy study
Evidence-backed gain

Validation of artificial intelligence-assisted CBCT analysis for predicting inferior alveolar nerve proximity to impacted mandibular third molars: a diagnostic accuracy study

This study aimed to evaluate the diagnostic accuracy of an artificial intelligence (AI)-assisted cone-beam computed tomography (CBCT) analysis system for predicting the spatial proximity of the inferior alveolar nerve (IAN) to impacted mandibular third molars (M3M), using expert radiologist assessment as the reference standard. A retrospective diagnostic accuracy study was conducted on an internal institutional cohort of 312 patients (mean age 28.21 ± 6.62 years; January 2021-December 2024). A deep learning syst…

Health
A novel approach for predicting heart failure survival using a rectangular coded network
Evidence-backed gain

A novel approach for predicting heart failure survival using a rectangular coded network

This paper provides both effortless augmentation of data and efficient creation of images according to the image input size of deep learning models by converting almost all numerical data into 24-bit images of particular standards. As cardiovascular disease is a cause of mortality, artificial intelligence-based architectures may play an important role here, and predicting survival from heart failure is a great challenge. For this purpose, we adjust the image input size according to different deep learning archit…

Health

Cross-Species Generalization and Comparative Performance Analysis of Deep Neural Network Architectures in Histological Image Classification

Histological image classification plays a critical role in biomedical research and diagnostic processes. Advances in the field of deep learning present significant opportunities for enhancing diagnostic accuracy and developing automated decision support systems. This study aims to comparatively evaluate the out-of-distribution generalization and cross-domain classification performance of different deep neural network encoders. In this study, models were trained on an internal dataset of 4307 hematoxylin and eosi…

Health
Cross-Species Generalization and Comparative Performance Analysis of Deep Neural Network Architectures in Histological Image Classification

AI Challenges and the Future of Education: A Needed Epistemic, Political, and Ecological Agenda for Critical Leadership Scholars and Practitioners

Artificial intelligence is already transforming contemporary education. Besides revolutionizing how students learn and educators teach it is also challenging the frameworks of knowledge and power that underpin leadership. Unfortunately, and because of an imposed discourse of urgency spread from big AI corporations and economic and political interests, AI has been rapidly integrated into educational settings without a profound and critical evaluation of its assumptions and consequences. This is the main reason wh…

Education
AI Challenges and the Future of Education: A Needed Epistemic, Political, and Ecological Agenda for Critical Leadership Scholars and Practitioners

Deepfake Anthony Albanese used in celebrity scams duping Australians out of $7.4m, Asic warns

There has been a steep rise in scammers luring victims into phoney investment opportunities using deepfakes of celebrities and politicians, Australia’s corporate watchdog has warned. And Anthony Albanese is the figure most commonly co-opted. Real footage of the prime minister, overlaid with fake audio promising Australians can invest $4,000 to earn $40,000 a month, appears in one video online. “This is not just another scam product,” the deepfake Albanese says, falsely describing it as an “official platform” wit…

Crime
Deepfake Anthony Albanese used in celebrity scams duping Australians out of $7.4m, Asic warns

A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large data…

Crime
A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

Real-time artificial intelligence-based anatomy recognition in single-port transvesical enucleation of the prostate

To evaluate the feasibility and accuracy of an artificial intelligence (AI) model to assist surgeons through automated real-time detection and segmentation of key anatomical structures during robot-assisted single-port transvesical enucleation of the prostate (STEP). This retrospective single-centre study utilised surgical videos from patients undergoing single-port robot-assisted transvesical prostate enucleation performed by a single expert surgeon. Selected frames extracted from these surgical videos were man…

Health
Real-time artificial intelligence-based anatomy recognition in single-port transvesical enucleation of the prostate

Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States

To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Retrospective diagnostic accuracy study. Three sites within a single US tertiary health system. Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 inc…

Health
Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States

Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness

Breathlessness is a common symptom in clinical practice, yet evidence for cost-effective strategies to diagnose the underlying health conditions causing breathlessness remains limited. Using Swedish population data with individuals with moderate to severe breathlessness, we developed an artificial intelligence (AI) reinforcement learning model to identify optimal, low-cost diagnostic pathways for breathlessness tailored to subgroups based on sex and smoking exposure. Sixteen clinically relevant conditions were d…

Health
Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness