Prevention of medical errors

Aggression in epilepsy and sleep: from historical accounts to brain networks and human behaviour.

For a long time, epilepsy has been associated with violent behaviour, acquiring a highly stigmatising reputation, shaped mainly by 19th-century medical theories that postulated a direct connection between epilepsy and the criminal personality. Such stigma exerts a negative effect on the quality of life of people with epilepsy and remains a significant concern in forensic settings. This narrative r...

Privacy-aware diabetic retinopathy grading and visual lesion-focused interpretability through mixture-of-experts federated deep learning with explainable AI.

Diabetic Retinopathy (DR) is still a major cause of vision loss that can be avoided. This means that we need automated screening systems that can work across institutions without putting sensitive medical data in one place. Although Federated Learning (FL) allows for cooperative model training while preserving data locality, Non-IID data distribution, communication overhead, and unstable convergen...

Identification and validation of BLK and OSBPL10 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma through machine learning algorithms.

Despite substantial advances in radiotherapy and chemotherapy for nasopharyngeal carcinoma (NPC), a subset of patients still develops metastasis or re...

Dual chaotic encryption method for wireless communication privacy data based on deep learning.

In wireless communication, the multipath effect and the time-varying channel due to mobility will directly lead to the key update cycle lagging far be...

Construction of a small-sample brain imaging data augmentation and explainable diagnostic model for autism based on generative adversarial networks.

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social communication deficits and repetitive st...

Machine learning based prediction models for first stroke in community primary care: a systematic review and meta-analysis.

BACKGROUND: Stroke is the third leading cause of death and the fourth leading cause of disability globally, particularly in low- and middle-income cou...

Biomechanical phenotypes of 90° change of direction in football players: Unsupervised machine learning in anterior cruciate ligament injury prevention.

PURPOSE: Neuromuscular and biomechanical deficits contribute to anterior cruciate ligament (ACL) injury risk in football. Artificial intelligence (AI)...

Solidarity or Segregation? ChatGPT Health and US Health Care Disparities.

ChatGPT Health, an artificial intelligence feature launched by OpenAI in January 2026, integrates personal medical records and consumer health data in...

Diagnostic yield of electroencephalographyin the emergency department: protocol for the EMINENCE-M multicentre retrospective observational study.

INTRODUCTION: Emergency EEG (emEEG) is increasingly used in the emergency department (ED), but its diagnostic yield remains uncertain. This protocol d...

Telesurgery and remote surgery research: a bibliometric analysis of scientific growth, collaboration, and thematic evolution.

Telesurgery and remote surgery have gained relevance in surgical innovation due to advances in robotics, telepresence, artificial intelligence, and hi...

Predicting Age-Related Facial Hyperpigmentation via Dermatologist Knowledge Elicitation and Generative Modeling.

INTRODUCTION: Facial hyperpigmentation is a primary marker of skin aging in Asian populations. While many artificial intelligence (AI)-based aging sim...

A blockchain-enabled trust-aware authentication framework for secure communication in VANETs.

Vehicular Ad Hoc Networks (VANETs) are essential for intelligent transportation systems; however, their dynamic topology and open wireless communicati...

Large language model applications in facial plastic and reconstructive surgery: a systematic review of applications, performance, and ethical considerations.

BACKGROUND: Artificial intelligence (AI)-powered large language models (LLMs) are increasingly used as adjunctive tools in education, research, and pa...

Development of a Conversational AI Intervention for Preventing Illegal Filming Among South Korean Adolescents: A Pediatric Health Perspective.

INTRODUCTION: This study aims to develop a Conversational AI-based illegal filming prevention education program (C-IFPE) for South Korean adolescents,...

Pilot of a blended learning model on medical students' communication skills in the context of digital innovations.

BACKGROUND: Effective physician-patient communication is essential for high-quality medical care. However, digital transformation and the increasing u...

The Role of Artificial Intelligence in the Prevention of Intimate Partner Violence and Sexual Violence: A Scoping Review of the Literature.

Intimate partner and sexual violence (IPV/SV) remain pervasive global public health issues. Prevention efforts typically occur at the primary level (p...

Predictive, Preventive and Personalized Medicine Approaches in Periodontitis: Emerging Technologies from Biomarkers to Artificial Intelligence-Driven Integrated Strategies.

RATIONALE AND PURPOSE: Periodontitis is a chronic multifactorial inflammatory disease linked to systemic conditions including cardiovascular disease a...

Assistive communication technologies for individuals with multiple disabilities: an evidence mapping review informing future AI-enabled AAC.

OBJECTIVE: Assistive communication technologies are central to participation for individuals with multiple disabilities. Although artificial intellige...

Comparative Evaluation of ChatGPT, Google Translate, and UD Talk for Chinese-to-Japanese Translation in Cardiology and Pulmonology Outpatient Consultations: Prospective Observational Study.

BACKGROUND: Language barriers between health care providers and patients can compromise communication quality, patient safety, and health care equity....

Optical communication network signal analysis and cyber security modelling by frequency modulation with machine learning algorithm.

Machine learning (ML) methods are commonly used in optical communication networks to enhance bandwidth analysis through efficient resource management ...