ReviewMultiple sclerosis (Houndmills, Basingstoke, England)2024
Integrating large language models in care, research, and education in multiple sclerosis management.
Review in Multiple sclerosis (Houndmills, Basingstoke, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.PLOS digital health · 2026Article
- Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Managing maternity: Moving care, not patients, using artificial intelligence (AI), internet-of-things (IOT) and point-of-care testing (POCT) devices.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Detecting progressing events in multiple sclerosis clinics in real-time is possible: No.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- MS360°: a conceptual digital-first, data-driven hybrid care framework for personalised multiple sclerosis management.NPJ digital medicine · 2026Review
- A Scoping Review on Artificial Intelligence-Supported Interventions for Nonpharmacologic Management of Chronic Rheumatic Diseases.Arthritis care & research · 2026Article
- Article
- Education Research: Can Large Language Models Match MS Specialist Training?: A Comparative Study of AI and Student Responses to Support Neurology Education.Neurology. Education · 2025Article
- Detecting New Lesions Using a Large Language Model: Applications in Real-World Multiple Sclerosis Datasets.Annals of neurology · 2025Article
- Evaluation of multiple generative large language models on neurology board-style questions.Frontiers in digital health · 2025Article
- Artificial intelligence and science of patient input: a perspective from people with multiple sclerosis.Frontiers in immunology · 2025Article
- Large language models as clinical decision-support tools in multiple sclerosis and neuromyelitis optica spectrum disorders: A comparative study of ChatGPT-4o and neurologists.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Use of techniques derived from generative artificial intelligence (AI), specifically large language models (LLMs), offer a transformative potential on the management of multiple sclerosis (MS). Recent LLMs have exhibited remarkable skills in producing and understanding human-like texts. The integration of AI in imaging applications and the deployment of foundation models for the classification and prognosis of disease course, including disability progression and even therapy response, have received considerable attention. However, the use of LLMs within the context of MS remains relatively underexplored. LLMs have the potential to support several activities related to MS management. Clinical decision support systems could help selecting proper disease-modifying therapies; AI-based tools could leverage unstructured real-world data for research or virtual tutors may provide adaptive education materials for neurologists and people with MS in the foreseeable future. In this focused review, we explore practical applications of LLMs across the continuum of MS management as an initial scope for future analyses, reflecting on regulatory hurdles and the indispensable role of human supervision.
Indexed as
Identifiers
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.