Military Medicine

Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation.

Research using artificial intelligence (AI) in medicine is expected to significantly influence the p...

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.

Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical ...

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios.

HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detec...

Telemedical percussion: objectifying a fundamental clinical examination technique for telemedicine.

PURPOSE: While demand for telemedicine is increasing, patients are currently restricted to tele-cons...

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology.

The artificial intelligence (AI) revolution is well underway, including in the medical field, and ha...

Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective.

In this article, we study activity recognition in the context of sensor-rich environments. In these ...

Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.

BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress ...

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms.

Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascula...

Should we replace radiologists with deep learning? Pigeons, error and trust in medical AI.

The sudden rise in the ability of machine learning methodology, such as deep neural networks, to ide...

Accelerating Development and Clinical Deployment of Diagnostic Imaging Artificial Intelligence.

Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, ...

Predicting outcome of daycare cognitive behavioural therapy in a naturalistic sample of patients with PTSD: a machine learning approach.

BACKGROUND: Identifying predictors for treatment outcome in patients with posttraumatic stress disor...

Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.

Many individuals visit rural telemedicine centres to obtain safe and effective health remedies for t...

Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.

PURPOSE: Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is ...

A Novel Automate Python Edge-to-Edge: From Automated Generation on Cloud to User Application Deployment on Edge of Deep Neural Networks for Low Power IoT Systems FPGA-Based Acceleration.

Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware ...

Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.

Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropi...

Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care.

Many experts have emphasised that chatbots are not sufficiently mature to be able to technically dia...

Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks.

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health manageme...

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually...

Robotics for neuroendovascular intervention: Background and primer.

The simultaneous growth of robotic-assisted surgery and telemedicine in recent years has only been a...

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health p...