AIMC Topic: Telemedicine

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Integrating prior knowledge with deep learning for optimized quality control in corneal images: A multicenter study.

Computer methods and programs in biomedicine
OBJECTIVE: Artificial intelligence (AI) models are effective for analyzing high-quality slit-lamp images but often face challenges in real-world clinical settings due to image variability. This study aims to develop and evaluate a hybrid AI-based ima...

Managing chronic disease in the digital era: The role of telemedicine apps and platforms.

Przeglad epidemiologiczny
Chronic diseases such as diabetes, heart disease, or asthma are one of the biggest health challenges of the modern world. Their management requires constant care and monitoring, which is a serious burden for healthcare systems. In the digital era, th...

Real-time health monitoring by examining the role of next-generation elements in a medical app.

Computers in biology and medicine
The healthcare sector is undergoing a profound transformation driven by the rapid rise in healthcare applications (mHealth apps), which are becoming integral to how patients manage their health. This paper examines the role of next-generation technol...

Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This study introduces multiscale feature learning to develop more robust and resilient activity recognition algorithms, aimed at accurately tracking and quantifying rehabilitation exercises while minimizing performance dispa...

Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm.

Scientific reports
With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote healt...

Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study.

JMIR formative research
BACKGROUND: Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (...

Cost-Effectiveness Analysis of a Machine Learning-Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study.

JMIR formative research
BACKGROUND: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. Howeve...

The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.

Herzschrittmachertherapie & Elektrophysiologie
Cardiovascular diseases remain one of the leading causes of death worldwide, placing a significant burden on individuals, families and healthcare systems. Telemedicine, in particular remote monitoring of patients with cardiovascular diseases, reduces...

Advancing Clinical Information Systems: Harnessing Telemedicine, Data Science, and AI for Enhanced and More Precise Healthcare Delivery.

Yearbook of medical informatics
OBJECTIVE: In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2023 in the CIS field.

Developing a standardized framework for evaluating health apps using natural language processing.

Scientific reports
Despite regulatory efforts, many smartphone health applications remain unregulated, raising concerns about privacy, security, and evidence-based effectiveness. The lack of standardized regulation has led to the proliferation of over 130 frameworks, i...