ArticleNeurology2023
Large Language Models in Neurology Research and Future Practice.
Article in Neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.European journal of neurology · 2026Article
- Large language models as a screening tool for the qualification of patients with Parkinson's disease for device-aided therapies.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Education Research: Bridging the Artificial Intelligence Training Gap: Evidence from a National Survey of Italian Neurology Residents.Neurology. Education · 2026Article
- Large Language Models in Clinical Neurology: A Systematic Review.Research square · 2026Article
- From diagnostics to education: Multi-domain evaluation of LLM chatbots in neurology.Journal of Taibah University Medical Sciences · 2026Review
- Large Language Models in Neurology Treatment Decision-Making: a Scoping Review.Journal of medical systems · 2025Article
- Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline.Journal of medical Internet research · 2025Review
- Evaluating the perspectives of ChatGPT and Gemini on glenohumeral osteoarthritis management.JSES international · 2025Article
- Ambient technology in epilepsy clinical practice.Epilepsia open · 2025Article
- Comparing Artificial Intelligence-Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study.Journal of medical Internet research · 2025Observational
- Fine-Tuning Large Language Models for Specialized Use Cases.Mayo Clinic proceedings. Digital health · 2025Review
- GPT meets PubMed: a novel approach to literature review using a large language model to crowdsource migraine medication reviews.BMC neurology · 2025Article
- Evaluation of multiple generative large language models on neurology board-style questions.Frontiers in digital health · 2025Article
- A simplified retriever to improve accuracy of phenotype normalizations by large language models.Frontiers in digital health · 2025Article
- Large language models for neurology: a mini review.Frontiers in digital health · 2025Review
- Evaluation of the accuracy of large language models in answering bone cancer-related questions.Frontiers in public health · 2025Article
- The Digitized Memory Clinic.Nature reviews. Neurology · 2024Review
- Artificial General Intelligence for the Detection of Neurodegenerative Disorders.Sensors (Basel, Switzerland) · 2024Review
- Integrating large language models in care, research, and education in multiple sclerosis management.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.