Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.

Yongcheon Kim1,2, Seung-Ah Choe3,4, Seogsong Jeong1

  • 1Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea.

Journal of Clinical Neurology (Seoul, Korea)
|July 9, 2026
PubMed
Summary

Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

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...
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...

You might also read

Related Articles

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

Sort by
Same author

Inverse association between metabolic dysfunction-associated steatotic liver disease and incident rheumatoid arthritis in middle-aged adults with chronic hepatitis B.

Clinical rheumatology·2026
Same author

Allergic Diseases and Risk of Incident Autoimmune Diseases: Phenotype-Specific Patterns and Multimorbidity Effects in a Nationwide Cohort.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology·2026
Same author

Acetaminophen exposure in an aging population and neurodegenerative outcomes.

European journal of clinical pharmacology·2026
Same author

Risk Prediction of Early-Onset Hepatocellular Carcinoma: Derivation and Validation in a Nationwide Young Adult Cohort.

Alimentary pharmacology & therapeutics·2026
Same author

Evaluating and Refining Claims-Based Algorithms for Pregnancy Outcomes and Gestational Age Estimation in Korea.

Journal of Korean medical science·2026
Same author

Prevalence and Relative Proportions of MS, NMOSD, and MOGAD in the Republic of Korea.

Neurology·2026
This summary is machine-generated.

Artificial intelligence (AI) in neurology shows promise but faces implementation challenges. The NEURAL framework offers a path for AI to improve clinical decisions and patient outcomes in real-world practice.

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Clinical Decision-Making

Background:

  • AI research in clinical neurology is extensive, yet few systems impact bedside decision-making.
  • Key barriers include unstable labels, narrow validation, workflow disconnection, and lack of monitoring.

Purpose of the Study:

  • To propose the NEURAL framework for developing practice-changing neurological AI.
  • To review current AI applications in neurology through the lens of the NEURAL framework.

Main Methods:

  • Literature review examining AI evidence across various neurological subspecialties.
  • Application of the NEURAL framework (Novel clinical insight, External and prospective validation, Utility over accuracy, Real-time workflow integration, Algorithmic transparency, Long-term outcome linkage).
Keywords:
artificial intelligenceclinical decision support systemsepilepsymachine learningneurodegenerative diseasesstroke

Related Experiment Videos

Main Results:

  • Acute stroke AI shows advanced clinical integration (e.g., large-vessel occlusion detection).
  • Other areas like EEG triage and sleep analysis show potential but require prospective validation.
  • Many AI models remain pre-implementation due to lack of validated clinical action.

Conclusions:

  • Neurological AI must move beyond pattern recognition to demonstrate actionability and workflow fit.
  • The NEURAL framework guides the development of clinically meaningful AI that improves patient care and health system benefits.