All stories

1313 published stories · page 22 of 88

HealthBoth readings

A Quality Assessment Rubric for Artificial Intelligence-Generated Patient-Friendly Radiology Reports

Background: Artificial intelligence (AI) tools are being used to translate radiology reports into plain language, but translation errors may compromise comprehension and safety. Objective: To develop and evaluate a rubric for assessing the quality and safety of AI-generated patient-friendly radiology reports. Methods: In this prospective study (conducted from February 2025 to December 2025), survey-workshop cycles, involving lay participants and a multidisciplinary panel, were used to develop a rubric for gradin…

American Journal of Roentgenology

Evidence-backed problem

AI Is Rewriting Biology And Medicine, But Human Oversight Remains Crucial

Evidence-backed problem

Sadiq Khan agrees to have his texts and emails searched in Palantir legal battle

Evidence-backed gain

Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology

Evidence-backed gain

Power of Green Capabilities and Artificial Intelligence (AI): Understanding How and When Green Innovation Promotes Sustainability

ABSTRACT Green innovation is increasingly recognized as a critical strategy for small‐ and medium‐sized enterprises (SMEs) to enhance sustainable performance. Drawing on the natural resource‐based view (NRBV), this study examines the impact of green innovation on sustainable performance and the mediating roles of green knowledge sharing and green dynamic capabilities. In addition, the moderating effect of artificial intelligence (AI) on these relationships was investigated. Data were collected from 230 SMEs in t…

Both readings

Efficiency vs. safety in AI-enabled medical education: an ethical analysis of AI as a bridge or a wedge

Abstract: Artificial intelligence is rapidly changing medical education, promising faster workflows and richer learning resources while quietly reshaping how future clinicians think and act. This paper examines the central tension between efficiency and safety in AI-enabled medical education, asking when AI functions as a bridge that strengthens training and when it becomes a wedge that undermi...

Evidence-backed problem

Machines that express emotions: from medical paternalism to machine paternalism?

Abstract: Introduction: Machines do not have emotions, but they can be programmed to express (to appear to have) certain emotions. In medicine, robots and AI machines are used that could express positive emotions (joy, compassion, hope, gratitude, inspiration or awe) and priorities. Objective: To describe the main ethical implications of integrating the expression of emotions in machines used f...

Evidence-backed gain

Causal adiposity and clinical validation of regional fat distribution in PMOS: a multi-modal analysis integrating GBD 2021, Mendelian randomization, and machine learning

Background Polyendocrine metabolic ovarian syndrome (PMOS), previously named polycystic ovary syndrome (PCOS), is the most common endocrine disorder among women of reproductive age and a leading cause of anovulatory infertility. However, the evolving global burden of PMOS and the role of adiposity, particularly regional fat distribution, remain incompletely understood. We integrated global epidemiological analysis, genetic causal inference, and clinical prediction modeling to investigate the burden and adiposity…

Health

A protocol for validation of novel artificial intelligence-based framework for dyspnoea investigation with cardiopulmonary exercise testing

Background Exertional dyspnoea largely represents the sensory translation of an ever-growing dynamic mismatch between ventilatory demand and capacity as exercise intensifies. This fundamental tenet, however, has not been formally incorporated into data display and clinical interpretation of incremental cardiopulmonary exercise testing (CPET). The objectives of the present study were to validate a novel framework (Dynamic Assessment of Dyspnoea and Ventilation on Exercise (DyVe-X)) to quantify the severity of exe…

Health

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and inconsistencies in existing diagnostic tools. This review evaluates the role of machine learning and deep learning approaches in improving ASD prediction, with a particular focus on two important yet r…

Health

Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model

Background To develop an artificial intelligence (AI)-assisted model for detecting skull fractures in neonates and infants using plain radiographs, enhancing diagnostic accuracy while minimizing radiation exposure. Methods A retrospective dataset of skull X-rays from 1,184 patients with head trauma (2010-2021) was collected. Images underwent preprocessing, including background removal, Gaussian blurring, binarization, and CLAHE-based contrast enhancement. Three convolutional neural network architectures (ResNet-…

Health

The AI arc and interpretive drift

Artificial intelligence now enters the clinical encounter at three points: before the visit, during clinical reasoning, and after it, when ambient tools generate the note. AI has the potential to adversely influence the diagnostic process at each of these steps. This opinion piece names interpretive drift, the subtle shift in meaning that occurs as a patient's account moves through an AI-generated summary and into the medical record. It argues that reviewing AI-generated documentation is a diagnostic safety prac…

Health

Species-level identification of Nocardia spp. from clinical samples via intelligent analysis of Raman spectroscopic fingerprints

Background Nocardia spp. are clinically opportunistic pathogens that are frequently underdiagnosed. They often lead to severe clinical consequences. These infections are often invasive, involving the lungs, nervous system, skin, and soft tissues. Different Nocardia spp. show significant differences in virulence and antimicrobial susceptibility. However, clinical manifestations are highly diverse, and species-level identification remains technically difficult. The precise diagnosis of Nocardia spp. is challenging…

Science

Machine learning insights into band gap properties in halide-based perovskites

Halide perovskites show great promise for applications in optoelectronic devices. The lead-free perovskites are attracting increasing interest due to their low toxicity and motivate the exploration of alternative compositions and structures, including A 2 BX 6 , A 2 BB'X 6 , A 3 B 2 X 9 , and A 4 BX 6 . Accurate predictions of a wide range of band gap energies are important for designing new materials. It is also important to generate a direct relationship between the structural and elemental descriptors and the…

Health

A Multiparametric Approach Integrating Multimodal Ultrasound and Serum TPO-Ab for Differentiating Thyroid Carcinoma in Hashimoto's Thyroiditis: A Comparative Diagnostic Study with Internal Validation

Objectives To evaluate whether a multiparametric approach combining grayscale ultrasound (2D-US), contrast-enhanced ultrasound (CEUS), and serum anti-thyroid peroxidase antibody (TPO-Ab) improves the differentiation of benign from malignant thyroid nodules (TNs) in patients with Hashimoto's thyroiditis (HT) and to assess the stability of the combined diagnostic model through internal validation. Methods This retrospective study enrolled 600 HT patients with 650 pathologically confirmed TNs. All patients underwen…