Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

Methods of Documentation VI: Case Management Model

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Artificial intelligence versus human consensus: A concordance analysis in the screening of studies for evidence synthesis in physical activity and sport.

Journal of bodywork and movement therapies·2026

Same author

Unveiling catalytic potential and the native role of PETase from Streptomyces sp.

Protein science : a publication of the Protein Society·2026

Same author

RRAEDy: adaptive latent linearization of nonlinear dynamical systems.

Scientific reports·2026

Same author

Descending Necrotizing Mediastinitis, When Mediastinal Drainage By Thoracoscopy Is Not Sufficient: Case Report and Review of the Literature.

Clinical medicine insights. Case reports·2026

Same author

Periodization of physical exercise in the rehabilitation of a professional soccer player following anterior cruciate ligament reconstruction: A case report.

Journal of bodywork and movement therapies·2026

Same author

Global Healthy and Sustainable City Indicators: Collaborative development of an open science toolkit for calculating and reporting on urban indicators internationally.

Environment and planning. B, urban analytics and city science·2026

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Artificial intelligence in multiple sclerosis management: Challenges in a new era.

Sebastián Rodríguez¹

¹Universidad Nacional de Colombia, Sede Bogotá. Facultad de Medicina. Departamento de Movimiento Corporal Humano, Maestría en Fisioterapia del Deporte y la Actividad Física, Colombia.

Multiple Sclerosis and Related Disorders

|April 11, 2024

Summary

Artificial intelligence (AI) offers advanced tools for diagnosing and treating multiple sclerosis (MS), improving accuracy and predicting outcomes. Responsible integration of AI in clinical practice is crucial for patient welfare and ethical healthcare.

Keywords:

Artificial intelligence Autoimmune diseases Deep learning Machine learning Multiple sclerosis

More Related Videos

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Related Experiment Videos

Last Updated: Jun 28, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

Published on: July 28, 2022

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Area of Science:

Neuroscience and Medical Informatics
Application of Artificial Intelligence in Healthcare

Background:

Multiple sclerosis (MS) presents significant diagnostic and therapeutic challenges, including high rates of misdiagnosis and difficulties in evaluating treatment efficacy.
Current diagnostic and treatment assessment methods for MS are often limited, necessitating innovative approaches.

Conclusions:

Artificial intelligence is a valuable, complementary resource for healthcare professionals managing multiple sclerosis.
Ethical considerations, including accountability and avoiding over-reliance, are paramount for AI implementation in MS care.
Responsible integration of AI technologies is essential to ensure patient welfare and advance multiple sclerosis research and treatment.