Prevention of medical errors

PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI

Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary co-creation workshop that inv...

Patient-Reported Challenges in Lymphoma Diagnosis: Analysis of Online Forum Narratives Using Artificial Intelligence

Lymphoma diagnosis remains challenging due to diverse subtypes and nonspecific presentations. While prior research focused primarily on clinical accuracy, how patients experience and describe these challenges remain understudied. This study systematically analyzed online patient narratives to investigate their perspectives on diagnostic difficulties. We developed an artificial intelligence (AI) pi...

Evaluating the acceptability, usability and clinical appropriateness of Your Path, an AI-powered tool facilitating relevant access to HIV services post-HIV self-testing in South Africa

Timely linkage to HIV prevention and treatment services following HIV self-testing (HIVST) remains a challenge in many countries. While HIVST offers p...

The Promise and Peril of Large Language Models in Digital Health: GPT-4 Personalizes Cardiovascular Patient Education but Amplifies Gender Biases

Gender-neutral patient education materials often overlook critical sex-based differences in cardiovascular disease (CVD). Large Language Models (LLMs)...

Predicting Carbapenem Resistance in Hospitalized Patients Using Machine Learning: A Retrospective Analysis of the MIMIC-III Database

Carbapenem-resistant Gram-negative bacteria (CR-GNB) represent a major health challenge due to limited therapeutic options, increased morbidity, and e...

Development and evaluation of a multivariate prediction model for diagnosing asthma in patients with clinically suspected asthma using capnography

Diagnosis of asthma in primary care is challenged by a multistep pathway with variable adherence leading to significant misdiagnosis, late diagnosis a...

“What witchcraft is this?”: Paramedics report gains in productivity, well-being, and patient flow from piloting ambient voice technology in an NHS Ambulance Service

UK ambulance services face record demand, resourcing challenges and rising clinical documentation burden. Ambient voice technology (AVT) coupled with ...

Machine learning-driven prediction of opioid and stimulant-related drug overdose fatalities: Analysis of the potential fourth wave

Between 2010 and 2021, fentanyl and stimulants co-involved deaths increased from 0.6% to 32.3% of all overdose deaths in the U.S. The Centers for Dise...

TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI

The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical pr...

Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading

Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as a...

Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging

Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiol...

Deep learning-enhanced signal detection for communication systems.

Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential chara...

Breast cancer pathology image recognition based on convolutional neural network.

This study presents a convolutional neural network (CNN)-based method for the classification and recognition of breast cancer pathology images. It aim...

Predicting metabolic syndrome: Machine learning techniques for improved preventive medicine.

Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribu...

HCAP: Hybrid cyber attack prediction model for securing healthcare applications.

The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) hav...

Detection and Prevention of Smishing Attacks

Phishing is an online identity theft technique where attackers steal users personal information, leading to financial losses for individuals and org...

CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks

Multi-band massive multiple-input multiple-output (MIMO) communication can promote the cooperation of licensed and unlicensed spectra, effectively e...

Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly i...

FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis

Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be hi...

A Machine Learning Approach for Emergency Detection in Medical Scenarios Using Large Language Models

The rapid identification of medical emergencies through digital communication channels remains a critical challenge in modern healthcare delivery, p...