Prevention of medical errors

Brain-computer interfaces inspired spiking neural network model for depression stage identification.

BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Interfaces inspired deep learning-assisted diagnosis based on physiological signals holds promise for improving traditional methods lacking physiological...

Assessment of the impacts of artificial intelligence (AI) on intercultural communication among postgraduate students in a multicultural university environment.

Artificial intelligence (AI) broadly influences different aspects of human life, especially human communication. One of the main concerns of the broad use of AI in daily interactions among different people could be whether it helps them interact easily or complicates their interactions. To answer the mentioned question, this study assessed the impacts of AI on intercultural communication among pos...

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions.

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology a...

Falls Prevention Using AI and Remote Surveillance in Nursing Homes.

The older population of United States is growing, with more adults having complicated medical conditions being admitted into nursing facilities and as...

Response to "Letter to the Editor: The Promise and Pitfalls of AI-Generated Anatomical Images: Evaluating Midjourney for Aesthetic Surgery Applications".

This response letter answers a query regarding our study on the use of the Midjourney app in aesthetic surgery. The original study questioned the util...

Digital twins: reimagining the future of cardiovascular risk prediction and personalised care.

The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the pot...

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis.

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well ...

Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagnosis of Bladder Cancer.

Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. However, the majority of the associated studies...

Explainable Federated Medical Image Analysis Through Causal Learning and Blockchain.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy...

Signal automatic modulation based on AMC neural network fusion.

With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectiv...

Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.

Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high h...

Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility.

The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with ...

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In...

Containment control for fractional-order networked system with intermittent sampled position communication.

This paper investigates containment control for fractional-order networked systems. Two novel intermittent sampled position communication protocols, w...

Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control.

PURPOSE OF REVIEW: This review examines the current state and future prospects of machine learning (ML) in infection prevention and control (IPC) and ...

The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives.

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML...

Federated learning using model projection for multi-center disease diagnosis with non-IID data.

Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect dat...

Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

OBJECTIVE: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals random...

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods ...

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics.

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensem...