+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
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Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

reproducibility and global accessibility of ultrasound imaging

Abstract: Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inherently restricts its reproducibility and global accessibility. Robotic ultrasound systems (RUSS) have evolved over the past 2 decades to mitigate these limitations by mechanically decoupling the human operator from the patient. Read more →

Journal of Ultrasound in Medicine
Gain

Robotic ultrasound systems improve reproducibility and global accessibility by decoupling the operator from the patient and using 5G telesonography to project diagnostic expertise.

Problem

Conventional ultrasound imaging's profound reliance on operator expertise restricts reproducibility and global accessibility.

Reading of the Month

AI has left the screen

A genome-writing model helped scientists design 16 viable bacteriophages. The result could lead to new treatments for antibiotic-resistant infections, but it also marks a new threshold: generated output can now become functioning biology.

Mosisa Saba · August 2026Read the essay →

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Health

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Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus
Evidence-backed gain

Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus

Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligence (AI)-assisted ultrasound of the rectus femoris (RF) offers a non-invasive approach for quantifying IMF. This study evaluated the association of IMF with diabetes-related complications (particularly diabetic nephropathy) and metabolic risk factors in patients with diabetes mellitus (DM). Materials and methods In this cross-sectional study, outpatients from a tertia…

Diabetes, Obesity and Metabolism

Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs

Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition and reader variability. This study developed stacked deep learning ensembles for automated detection and radiographic size stratification of periapical lesions. In total, 146 radiographs comprising normal cases and three lesion-size categories were cropped around the root apex and augmented using predefined transformations. Five convolutional neural network backbones were trained, and their probability out…

Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs

Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inherently restricts its reproducibility and global accessibility. Robotic ultrasound systems (RUSS) have evolved over the past 2 decades to mitigate these limitations by mechanically decoupling the human operator from the patient. This comprehensive review examines the historical trajectory of medical ultrasonography and robotics, highlighting their convergence into modern RUSS. We detail the taxonomies of ro…

Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction

Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally developed for patients with type 2 diabetes, to evaluate its generalizability in predicting 1-year hospitalization risk in a disease-agnostic adult cohort, with attention to fairness and explainability. Materials and methods The study utilized de-identified electronic health record data from a retrospective cohort of 17 857 adult patients at the University of Florida Health. The original iPsRS framework wa…

Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction

Diagnosing melioidosis and tracking treatment outcomes using breath

Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients' breath that can aid in diagnosing meli…

Diagnosing melioidosis and tracking treatment outcomes using breath

Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical diagnosis, where class imbalance and non-linear decision boundaries are common. Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Cl…

Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Education

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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Evidence-backed gain

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, re…

Education Sciences

Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program

Introduction The integration of artificial intelligence (AI) tools into radiation therapy workflows offers significant opportunities to improve efficiency by automating tasks such as contouring organs at risk (OARs). However, this also raises concerns regarding future practitioners' ability to critically evaluate auto-generated contours. This educational perspective examines how OAR contouring education can be integrated into undergraduate radiation therapy programs to support the development of foundational con…

Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program

Simulating Reality and Transforming Endoscopic Education-Lessons from Esophageal Dilation

Although endoscopic training has traditionally relied on mentorship and procedural volume to assess proficiency, the major gastroenterology societies have increasingly adopted competency-based assessment tools, including the Assessment of Competency in Endoscopy (ACE) and Direct Observation of Procedural Skills (DOPS), to provide a more objective evaluation of technical and cognitive skills. Despite these advances, mentor-based assessment remains susceptible to subjectivity. Emerging technologies such as simulat…

Simulating Reality and Transforming Endoscopic Education-Lessons from Esophageal Dilation

We must reject any notion of AI consciousness | Letters

Anil Seth is right to point out that to overestimate artificial intelligence is to underestimate ourselves (Once again we are told AI may be conscious – I study consciousness, and I have my doubts, 15 July). But he is wrong only to have doubts about whether AI systems like Claude may become conscious. He should be certain, and should say so more forcefully. It’s not like anything to be Claude, just as it’s not like anything to be a washing machine. An academic education is not required to realise that, common se…

We must reject any notion of AI consciousness | Letters

Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning

Since its maiden release into the public domain on November 30, 2022, ChatGPT garnered more than one million subscribers within a week. The generative AI tool ⎼ChatGPT took the world by surprise with it sophisticated capacity to carry out remarkably complex tasks. The extraordinary abilities of ChatGPT to perform complex tasks within the field of education has caused mixed feelings among educators, as this advancement in AI seems to revolutionize existing educational praxis. This is an exploratory study that syn…

Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning

Dubai launches first accredited Bachelor of Music programme with AI focus; course to begin in September

Dubai is set to offer its first accredited Bachelor of Music programme starting September 2026. This new course focuses on contemporary and electronic music, covering production and business. Students will gain practical experience in professional studios and work on industry projects. Artificial intelligence in music creation will also be a key part of the curriculum. SAE University College Dubai is accepting applications now for this exciting opportunity.

Dubai launches first accredited Bachelor of Music programme with AI focus; course to begin in September

Media & Arts and the evidence leaderboard

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Top evidence-backed gains

  1. An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy: Results IMIL-Net achieved the highest diagnostic performance, with a mean area under the curve (AUC) of 0.975 (95% CI 0.959-0.991), accuracy of 0.915 (95% CI 0.883-0.947), sensitivity of 0.876 (95% CI 0.803-0.949), and specificity of 0.945 (95% CI 0.910-0.980).Score 84 · 1 sources
  2. Systematic review of 35 studies found classical AI models for bipolar disorder achieved pooled AUC 0.80 for long-term maintenance response and 85%-97% accuracy for safety and dose optimization.Score 84 · 1 sources
  3. Adopting best practices for AI servers in the USA could cut projected carbon emissions by up to 73% and water footprints by up to 86% between 2024 and 2030.Score 84 · 1 sources

Top evidence-backed problems

  1. In the same tertiary GDD cohort, the Youden-optimal model missed 33.9% of children who progressed to ID and had NPV 31.7%, limiting safe rule-out and indicating poor transportability beyond high-prevalence referral settings.Score 83 · 1 sources
  2. Even the domain-specific M4CXR model was inconsistent with reference findings in 25.2% of chest radiograph cases and did not significantly change RADPEER discrepancy rates versus original interpretation.Score 82 · 1 sources
  3. Fully automated cardiomegaly screening accuracy dropped to 76.07% on the external OpenI dataset, showing domain-shift vulnerability, while manual CTR measurement remains a clinical bottleneck and existing deep models suffer from algorithmic bloating.Score 82 · 1 sources